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2015MNRAS.450.4505H__Weiss_et_al._2013_Instance_1

In the case of systems detected via transits, passage through an inclination resonance is likely to prevent a planet from being observed in transit, assuming the natal planetary system is edge-on in the first place. Such excitations may explain inner holes in otherwise densely packed systems observed by Kepler. An example is the Kepler-11 system (Lissauer et al. 2011b), which shows six planets, five of which are between 0.09 and 0.3 au, but none interior to 0.09 au. If we examine the secular architecture of this system assuming the presence of a seventh, 1 M⊕ planet located in the interior hole,1 we find avoided crossings in both eccentricity and inclination near semi-major axes ∼0.065 au, and other secular inclination resonances at 0.0233 and 0.0763 au. Thus, it is possible that Kepler-11 contains a low-mass planet in the observed inner ‘hole’, but which is inclined relative to the outer planets. The inclination could be substantial if the current period is 8 d. The Kepler planet sample contains a large number of multiple planet systems with potential for secular resonances. KOI-94 (Weiss et al. 2013) is interesting because of the presence of a Saturn mass planet in a similar orbit to 55 Cancri. The innermost planet lies just outside a pair of secular resonances at 3.0 d (eccentricity) and 3.4 d (inclination) and so is consistent with a lack of inclination excitation. If we allow for the existence of a 1 M⊕ planet interior to this, eccentricity resonances are found at 0.9 and 2.1 d (and a mean motion resonance at 1.9 d) and inclination resonances at 1.2 and 2.1 d. Thus, a planet that has tidally migrated to orbital periods 2 d in this system could have had its inclination pumped up and have avoided transit. Another compact multiplanet system containing a massive planet is Kepler-30 (Fabrycky et al. 2012). However, the only resonances encountered by a tidally migrating 1 M⊕ inner planet lie at 14.8 d (both inclination and eccentricity) and at 13.9 d (a mean motion resonance). These may be too far out to be strongly affected by tides. Kepler-9 (Holman et al. 2010) shows two Saturn-class planets and an interior low-mass planet, that we will assume to be 1 M⊕ again. Since this inner planet does indeed transit, we must assume little inclination excitation has taken place in this system. Indeed, the sole inclination resonance for this configuration lies at 7.5 d, so that the planet would not experience any excitation if it started the migration interior to this point. Eccentricity resonances are found at 1.0 and 2.5 d, bracketing the observed period. Kepler-51 (Steffen et al. 2013) is an example of a compact Kepler planetary system with a large inner hole but no Jovian-class planets. In this case, an inner 1 M⊕ planet experiences no secular resonances. There is a frequency commensurability at 2.9 d, but no avoided crossing because the modes are almost completely decoupled (one is driven primarily by the relativistic precession).

2021ApJ...923L..22A__Rosado_et_al._2015_Instance_3

Pulsar timing experiments (Sazhin 1978; Detweiler 1979) allow us to explore the low-frequency (∼1–100 nHz) part of the gravitational-wave (GW) spectrum. By measuring deviations from the expected arrival times of radio pulses from an array of millisecond pulsars, we can search for a variety of GW signals and their sources. The most promising sources in the nanohertz part of the GW spectrum are supermassive binary black holes (SMBHBs) that form via the mergers of massive galaxies. Orbiting SMBHBs produce a stochastic GW background (GWB; Lommen & Backer 2001; Jaffe & Backer 2003; Volonteri et al. 2003; Wyithe & Loeb 2003; Enoki et al. 2004; Sesana et al. 2008; McWilliams et al. 2012; Sesana 2013; Ravi et al. 2015; Rosado et al. 2015; Kelley et al. 2016; Sesana et al. 2016; Dvorkin & Barausse 2017; Kelley et al. 2017; Bonetti et al. 2018; Ryu et al. 2018), individual periodic signals or continuous waves (CWs; Sesana et al. 2009; Sesana & Vecchio 2010; Mingarelli et al. 2012; Roedig & Sesana 2012; Ravi et al. 2012, 2015; Rosado et al. 2015; Schutz & Ma 2016; Mingarelli et al. 2017; Kelley et al. 2018), and transient GW bursts (van Haasteren & Levin 2010; Cordes & Jenet 2012; Ravi et al. 2015; Madison et al. 2017; Islo et al. 2019; Bécsy & Cornish 2021). We expect to detect the GWB first, followed by detection of individual SMBHBs (Siemens et al. 2013; Rosado et al. 2015; Taylor et al. 2016; Mingarelli et al. 2017) that stand out above the GWB. Detection of GWs from SMBHBs will yield insights into galaxy mergers and evolution not possible through any other means. Other potential sources in the nanohertz band include cosmic strings (Damour & Vilenkin 2000, 2001; Berezinsky et al. 2004; Damour & Vilenkin 2005; Siemens et al. 2006, 2007; Ölmez et al. 2010; Sanidas et al. 2013; Blanco-Pillado et al. 2018; Chang & Cui 2021; Ghayour et al. 2021; Gorghetto et al. 2021; Wu et al. 2021a; Blanco-Pillado et al. 2021; Lin 2021; Chiang & Lu 2021; Lazarides et al. 2021; Chakrabortty et al. 2021; Ellis & Lewicki 2021), phase transitions in the early universe (Witten 1984; Caprini et al. 2010; Addazi et al. 2021; Arzoumanian et al. 2021; Di Bari et al.2021; Borah et al. 2021; Nakai et al. 2021; Brandenburg et al.2021; Neronov et al. 2021), and relic GWs from inflation (Starobinskiǐ 1979; Allen 1988; Lazarides et al. 2021; Ashoorioon et al. 2021; Yi & Zhu 2021; Li et al. 2021; Poletti 2021; Vagnozzi 2021; Sharma 2021), all of which would provide unique insights into high-energy and early-universe physics.

2019AandA...631A..35B__Bridges_et_al._(1996)_Instance_3

The collision velocity dependence of the coefficient of restitution between particles was observed in experiments (Bridges et al. 1996; Higa et al. 1996) and is discussed in the literature (e.g., Ramírez et al. 1999; Zhang & Vu-Quoc 2002). However, the experiments by Heißelmann et al. (2010), used in the present paper to support our assumption of a constant coefficient of restitution, do not see a variation of the coefficient of restitution between particles at low collision velocities (≤ 1 cm s−1). This discrepancy in results might originate in the nature of the collisions studied in these different experiments: Bridges et al. (1996) and Higa et al. (1996) performed collisions of a particle with a flat surface, while Heißelmann et al. (2010) observed particle-particle collisions in a free-floating environment. The latter is an experimental environment very similar to NanoRocks. In such inter-particle collisions in free-floating environments, other physical effects lead to a different behavior of the energy dissipation during collisions. In particular, the damping behavior of a large plate or surface is expected to differ from that of a same-sized particle, so that the velocity dependence of the coefficient of restitution might be an effect of the experimental setup in Bridges et al. (1996) and Higa et al. (1996). Colwell et al. (2016) and Brisset et al. (2018) studied collisions between a round cm-sized particle and a flat surface of fine grains. They also observed an increase of the coefficient of restitution with decreasing collision velocity. While the composition of the target surface was different than in Bridges et al. (1996) and Higa et al. (1996) (fine granular material vs. solid ice), the similar behavior of the coefficient of restitution supports the fact that particle-surface collisions are very different from particle-particle collisions, and coefficients of restitution are only velocity dependent for collisions with particles with very different sizes (a much larger particle can be approximated as a target surface).

2016ApJ...822...72C__Liu_et_al._2010_Instance_1

Here, for the first time, we have identified highly dynamic non-potential activity on QS-like supergranular network scales. These events overlie mixed polarity network flux elements near the spatial resolution of HMI, and are the first non-potential structures to be unassociated with strong concentrations of bipolar magnetic flux. One event (2011b August 05) shows eruptive activity in the form of jets, which is similar to larger-scale micro-sigmoids (Raouafi et al. 2010) and even QS bright point sigmoids (Chesny et al. 2015). The existence of flaring non-potential fields in the QS-like mixed network immediately shows that supergranular-scale magnetic fields can support processes similar to sigmoid formation (Chesny et al. 2013). Strong non-potential field arcades have been observed in hot X-ray sigmoids on scales of hundreds of arseconds (Moore et al. 2001; Gibson et al. 2002; Liu et al. 2010), micro-sigmoids in soft X-ray imaging on scales of ∼50″ (Mandrini et al. 2005; Raouafi et al. 2010) and small-scale AR EUV (Zheng et al. 2012, 2013), and now at EUV temperatures in QS-like mixed network fields with lengths down to ∼10″. This range of lengths over a range of temperatures and magnetic field topologies points directly to self-similar mechanisms influencing plasma and magnetic field dynamics at a range of scales. Our findings suggest that the processes driving some large-scale eruptions (i.e., flux emergence, helicity build-up, and flux cancellation leading to non-potential field heating (Chen et al. 2014)), can also manifest in a range of configurations on sub-network size scales in QS-like magnetic field configurations. These QS flaring non-potential fields are similar to their large-scale counterparts, but not as strict in their evolution. The diversity in the observed topologies may scale with the diversity of magnetic configurations that exist in the supergranulation network. QS non-potential fields evolve in multi-polar environments, and are not restricted to strongly bipolar dominated regions as in larger-scale, higher temperature events. Despite this, one of the presented events (2011a August 05) results in a post-flare potential loop arcade, which is similar to some observed AR sigmoid fields (Moore et al. 2001).

2016AandA...588A...2L__Mätzler_(1998)_Instance_1

H2O ice on Pluto has long escaped spectroscopic detection, and based on initial New Horizons data appears to be exposed only in a number of specific locations, usually associated with red color, suggestive of water ice/tholin mix (Grundy et al. 2015; Cook et al. 2015). Nonetheless, water ice is likely to be ubiquitous in Pluto’s near subsurface, given its cosmogonical abundance, Pluto’s density, and its presence on Charon’s surface6. Absorption coefficients for pure water ice (kH2O) at sub-mm-to-cm wavelengths are discussed extensively by Mätzler (1998), who also provides several analytic formulations to estimate them as a function of frequency and temperature along with illustrative plots. We use the Mishima et al. (1983) formulation (see Appendix of Mätzler 1998). Its applicability is normally restricted to temperatures above 100 K, but Fig. 2 of Mätzler (1998) indicates the trend with temperature. Absorption coefficients extrapolated to 50 K (estimated as half the values at 100 K) are shown in Fig. 5. At 500 μm, our best estimate is kH2O = 0.25 cm-1, comparable to the above values for CH4 and N2 ices. The corresponding penetration length is therefore comparable to the diurnal skin depth but remains negligible compared to the seasonal skin depth, even for seasonal Γ = 25 MKS. According to these calculations, the seasonal layer would be probed only at a wavelength of ~4 mm and beyond. We also remark that the expression from Mishima et al. (1983) would give a penetration depth of 56 m at 2.2 cm, which is an order of magnitude larger than indicated by the laboratory measurements of Paillou et al. (2008). In addition, small concentrations of impurities can dramatically reduce the microwave transparency of water ice (e.g., Chyba et al. 1998 and references therein). Therefore, the above calculations likely indicate upper limits to the actual penetration depth of radiation in a H2O ice layer, from which we conclude that the seasonal layer is not reached at the Herschel wavelengths.

2021AandA...655A..99D__Carigi_et_al._2005_Instance_2

Another way of obtaining information about the nucleosynthesis processes involved in producing carbon is to compare it with other elements that are characterised by a well-known source of production, as in the case of oxygen. In Fig. 5, we show the variation of [C/O] as a function of [Fe/H], which serves as a first-order approximation to the evolution with time. To calculate the [C/O] ratios, two oxygen abundance indicators are used independently. At subsolar metallicities, the abundance ratios with both oxygen indicators are mostly negative and show an increasing trend towards higher metallicity. This is explained by the fact that oxygen is entirely produced by SNe Type II from massive progenitors, which started to release theiryields at earlier ages in the Galaxy and, hence, at lower metallicities (e.g. Woosley & Weaver 1995). The massive stars producing carbon at low metallicities might be less massive than those producing oxygen (i.e. having a longer life), explaining a delayed contribution of carbon, hence, the negative [C/O] ratios. Alternatively, this could be explained by increasing O/C yields for more massive progenitors of SNeII. Once metallicity starts to increase, low- and intermediate-mass stars release carbon and massive stars start to eject more carbon than oxygen (Carigi et al. 2005). The [C/O] ratio seems to have a constant rise towards higher metallicities when using the forbidden oxygen line. However, in the case when the O I 6158 Å line is employed, we do observe that the maximum in [C/O] takes places close to solar metallicity to then become flat or decrease. This suggests that low-mass stars mostly contribute to carbon around solar metallicity, whereas at super-solar metallicities, massive stars produce carbon together with oxygen, thereby flattening or even decreasing the [C/O] ratio. This trend is in agreement with the metallicity dependent yields from Carigi et al. (2005), which provide higher carbon as [Fe/H] increases from massive stars (i.e. also increasing the O production) but lower carbon from low and intermediate mass stars as [Fe/H] increases (i.e. less production of C). The turning point of increased relative production of carbon from massive stars takes place at A(O) ~ 8.7 dex (see Fig. 2 of Carigi et al. 2005) which equals to [O/H] ~ 0.0 dex. This observed behaviour of [C/O] is in contrast to the steady increase of [C/O] up to [Fe/H] ~ 0.3 dex found, for example, by Franchini et al. (2021). Nevertheless, the general trend we find when using the [O I ] 6300 Å line is similar to the reported by Franchini et al. (2021), who use also that oxygen indicator. All thick-disk stars present negative [C/O] ratios and when using the oxygen line at 6158 Å thin-disk stars with [Fe/H] ≲ –0.2 have [C/O] 0 as well. Thick-disk stars and low-metallicity thin-disk stars at the same metallicity have similar [C/O] ratios, meaning that the balance between different production sites for oxygen and carbon is the same among both populations, despite [C/Fe] and [O/Fe] being systematically higher for thick-disk stars at a given metallicity.

2021MNRAS.500.3002B__Kremer_et_al._2020_Instance_1

It is as well of wide interest and diverse implications (e.g. Abadie et al. 2010; Mandel & Farmer 2017) to consider how and under which conditions NSs and BHs would pair up in tight-enough binaries so that they can spiral in by emitting GW radiation and merge within the Hubble time. Recent numerical studies based on analytical (Hénon 1975), direct N-body integration (Aarseth 2003), and Monte Carlo approach (Hénon 1971; Joshi, Rasio & Portegies Zwart 2000; Hypki & Giersz 2013) show that the retention of BHs in dense stellar clusters of wide mass range, beginning from low-/medium-mass young and open clusters (e.g. Banerjee, Baumgardt & Kroupa 2010; Ziosi et al. 2014; Mapelli 2016; Banerjee 2017, 2018a, b; Park et al. 2017; Di Carlo et al. 2019; Kumamoto, Fujii & Tanikawa 2019; Rastello et al. 2019) through globular clusters (e.g. Breen & Heggie 2013; Morscher et al. 2013; Sippel & Hurley 2013; Arca-Sedda 2016; Hurley et al. 2016; Rodriguez, Chatterjee & Rasio 2016; Wang et al. 2016; Askar et al. 2017; Chatterjee, Rodriguez & Rasio 2017a; Chatterjee et al. 2017b; Fragione & Kocsis 2018; Rodriguez et al. 2018; Antonini & Gieles 2020; Kremer et al. 2020) to galactic nuclear clusters (e.g. Antonini & Rasio 2016; Hoang et al. 2018, 2019; Antonini, Gieles & Gualandris 2019; Arca-Sedda & Capuzzo-Dolcetta 2019; Arca Sedda 2020), comprise environments where BHs can pair up through close dynamical interactions, which, furthermore, lead to general-relativistic (hereafter GR) coalescences of these BBHs. Being much more massive than the rest of the stars, the BHs, which remain gravitationally bound to a cluster after their birth, spatially segregate and remain highly concentrated in the cluster’s innermost (and densest) region (e.g. Banerjee et al. 2010; Morscher et al. 2015) due to dynamical friction (Chandrasekhar 1943; Spitzer 1987) from the stellar background. This is essentially an early core collapse of the cluster leading to its post-core-collapse behaviour (Hénon 1975; Spitzer 1987; Heggie & Hut 2003), i.e. energy generation in the ‘collapsed’ BH core leading to an overall expansion of the cluster with time (Breen & Heggie 2013; Antonini & Gieles 2020). Inside this core, BHs undergo close binary–single and binary–binary encounters giving rise to compact subsystems (triples, quadruples, or even higher order multiples) whose resonant evolution can lead to GR inspiral and merger of their innermost binaries (Leigh & Geller 2013; Samsing, MacLeod & Ramirez-Ruiz 2014; Geller & Leigh 2015; Banerjee 2018b; Samsing 2018; Zevin et al. 2019), through the binaries’ eccentricity pumping. The breakup of such subsystems or simply close, flyby encounters may also lead to a sufficient boost in eccentricity of a BBH such that it merges either promptly, in between two close encounters (e.g. Kremer et al. 2019) or within a Hubble time if it gets ejected from the cluster as a result of the interaction (e.g. Rodriguez et al. 2015; Park et al. 2017; Kumamoto et al. 2019). Note that such GR mergers can also happen in hierarchical systems, containing NSs and BHs, in a galactic field that derive from field massive-stellar multiplets (e.g. Toonen, Hamers & Portegies Zwart 2016; Antonini, Toonen & Hamers 2017; Fragione & Loeb 2019; Fragione, Loeb & Rasio 2020b).

2020AandA...641A..85S__Orienti_&_Dallacasa_2008_Instance_1

To derive the equipartition magnetic field of J1146+4037, we predict the rest-frame 8.4 GHz (redshifted to 1.4 GHz at z = 5.0059) flux density from our spectral model. However, there is no source size measurement at 1.4 GHz. We make use of the full width at half maximum (FWHM) source size of 0.74 ± 0.01 mas derived by the Gaussian fit from 5 GHz VLBI mas angular resolution observations (Frey et al. 2010). We note that in our calculations, we assume a source size that is 1.8 times larger than the FWHM, following the approach of Readhead (1994) and Orienti & Dallacasa (2008). The derived equipartition magnetic field is 34 − 7 + 8 $ 34^{+8}_{-7} $ mG. This is within the range of the equipartition magnetic fields of 17 HFP radio sources (7–60 mG; quasars and galaxies at 0.22   z   2.91; Orienti & Dallacasa 2012) and 5 HFPs at 0.084   z   1.887 (18–160 mG; Orienti & Dallacasa 2008). The magnetic field calculated from the turnover information listed in Table 4 is 1 . 8 − 2.7 + 2.3 $ 1.8^{+2.3}_{-2.7} $ G assuming an SSA origin with Eq. (3), however the uncertainty is very large. The large uncertainty is caused by the fact that we only have four data points to constrain the turnover information and we do not have source size measurements at the turnover frequency, but rather we assume the source size measured at another frequency. More data taken in other wavelength bands are needed to meaningfully constrain the turnover peak, and mas resolution observations at the peak frequency are needed to give reliable magnetic field strength measurements. This may indicate that the turnover is not caused by SSA, by comparing the large magnetic field strength measured from the spectral turnover ( 1 . 8 − 2.7 + 2.3 $ 1.8^{+2.3}_{-2.7} $ G) with the equipartition magnetic field strength ( 34 − 7 + 8 $ 34^{+8}_{-7} $ mG). As J1146+4037 is a strong blazar, the turnover may be caused by its strong variability. Another possible explanation for the spectral turnover is that high-density plasma in the nuclear region attenuates the radio emission from the central active BH. High-resolution, interstellar medium observations of the nuclear region of this target may address the latter issue.

2022AandA...663A..77N__Krolik_et_al._1981_Instance_1

In our model, we used a non-hydrodynamical approach based on assumptions of the motion of separate clouds under gravity and the action of radiation pressure acting on dust. This has considerable limitations but they are justified as the first approximation for modeling the LIL part of the BLR. As discussed in the classical paper of line-driven wind model (for HIL part of BLR) by Murray et al. (1995), the optical depth of the emitting region must be moderate (column density of order of 1023 cm−2) and the local density is high (for LIL part it is higher than that of HIL, many authors argue for a local density about 1012 cm−3, for instance, Adhikari et al. 2016; Baskin & Laor 2018; Panda et al. 2018), while the BLR is extended. There are two possibilities to support a consistent picture: it is either to assume a very narrow stream of material flowing out, with the cross-section on the order of 1012 cm, as in Murray et al. (1995), where they assume lower density so the size is actually larger 1014 cm; or to assume considerable clumpiness of the medium. We followed the second approach since there are natural thermal instabilities in the plasma, such as instability caused by X-ray irradiation (Krolik et al. 1981). In this case, the plasma spontaneously forms colder clumps (at a temperature of ∼104 K, cooled through atomic processes) embedded in a hotter medium (at temperature ∼107 K, set at an inverse Compton temperature value). The presence of highly or fully ionized medium is aptly supported by the observations as well as by the theory. Then the two media of density contrast of order of 103 provide the rough pressure balance. Blandford et al. (1990) discussed the typical values for ionization parameter in AGN clouds covering the range from 10−3 to 1, that is, up to four orders of magnitudes. The precise description of the structure of the clumpy medium is very difficult. Even in the case of a single cloud exposed to irradiation, in plane-parallel approximation requires a radiative transfer to be performed, which would then show the gradual change in the density, temperature, and ionization parameters (see e.g., Baskin & Laor 2018; Adhikari et al. 2018), with the low density first and the temperature roughly at inverse Compton temperature (depending on the shape of the incident spectrum), and then a relatively rapid decrease at the subsequent ionization fronts. A proper description of this transition, calculated under constant pressure, actually requires inclusion of the electron conduction (e.g., Begelman & McKee 1990; Różańska & Czerny 2000). Deep within the cloud, there is a further drop in temperature and a rise in density due to a decrease in the local flux as a result of absorption. As emphasized by Baskin & Laor (2018), radiation pressure also plays a dynamical role in this process. The picture is further complicated if plane-parallel approximation is abandoned. The presence of the numerous clouds of complex shapes can be fully consistent with simple estimates of the cloud number based on line shape properties as done by Arav et al. (1998). Of course, there are also certain processes that can lead to cloud destruction, such as the action of tidal forces (Müller et al. 2022), Kelvin–Helmholtz instabilities, and cloud ablation), however, the destruction rate can be strongly affected by the magnetic field (e.g., McCourt et al. 2015). The relative importance between the condensation rate and destruction rate depends on the cloud size, as it is set roughly by the field length (Field 1965). Cloud formation in AGN has been seen in the numerical simulations from Waters et al. (2021), but at distances much greater than the BLR distance which was likely related to the numerical setup and the requested spatial resolution of the computations. The issue is thus extremely complex, and simple order-of-magnitude estimates based on a single density and single temperature of the cloud and intercloud medium are not fully adequate and cannot reproduce the full ionization parameter range. However, addressing this point in detail is beyond the scope of the current paper.

2016MNRAS.458.2870V__Busha_et_al._2011_Instance_1

Unfortunately, since the study by Onions et al. only used a single dark matter halo, albeit at exquisite numerical resolution, the comparison is limited to the relatively low-mass end of the subhalo mass function (SHMF), where the cumulative mass function N( > m), exceeds unity. In order to study the massive end of the SHMF, where N(>m) 1, one needs to average over large numbers of host haloes. The abundances of these rare but massive subhaloes has important implications for, among others, the statistics of massive satellite galaxies (e.g. Boylan-Kolchin et al. 2010; Busha et al. 2011) and the detection rate of dark matter substructure via gravitational lensing (e.g. Vegetti et al. 2010, 2012). In this paper, the second in a series, we use subhalo mass functions (SHMFs) and subhalo catalogues from a variety of numerical simulations that are publicly available, and that have been obtained using different subhalo finders, to compare SHMFs, focusing on the massive end. We confirm the findings by Onions et al., that the SHMFs are consistent at the 20 per cent level at the low-mass end. At the massive end, though, different subhalo finders yield subhalo abundances that differ by more than one order of magnitude! By comparing the simulation results with a new, semi-analytical model (Jiang & van den Bosch 2016, hereafter Paper I), we demonstrate that subhalo finders that identify subhaloes based purely on density in configuration space, such as the popular subfind and bdm, dramatically underpredict the masses, but not the maximum circular velocities, of massive subhaloes. We also show that the model predictions are in excellent agreement with the simulation results when they are analysed using more advanced subhalo finders that use phase-space and/or time domain information in the identification of subhaloes. We discuss a number of implications of our findings, in particular with regard to the power-law slope of the subhalo mass and velocity functions.

2020ApJ...892..103Z__Melia_&_Shevchuk_2012_Instance_1

Similarly, according to Equation (2), angular diameter distances can be calculated from observed angular sizes as 7 and we also treated the length scale lm as a free parameter. In order to test the CDDR using different samples one should use a redshift matching criterion Δz 0.005 (Li et al. 2011; Liao et al. 2016). However, it turned out difficult to fulfill this criterion in order to compare distances derived from QSO [XUV] and QSO [CRS] directly. Therefore, proceeding in a similar manner as Li & Lin (2018), we reconstruct QSO [CRS] angular size as a function of redshift from the binned data. The angular size of intermediate-luminosity quasars was grouped into 20 redshift bins of width Δz = 0.1 starting from the smallest redshift of this sample. Median values of angular size plotted against the mean redshift in each bin are shown in Figure 4. The python package GaPP based on Gaussian Processes (Seikel et al. 2012) was used for the reconstruction process that depends on the mean function and the covariance function . In order to discuss the influence of the choice of these two prior functions, we studied four mean functions and three covariance functions. The prior mean functions that we discussed are the following: zero mean function, the theoretical function of angular size calculated from the angular diameter distance under the assumption of three cosmological models: flat ΛCDM with Ωm = 0.27, so-called Rh = ct universe (Melia & Shevchuk 2012), and a Mirage model with Ωm = 0.27, w0 = −0.7 and w1 = −1.09 (Shafieloo et al. 2012). The linear size scaling factor lm = 11.42 pc was assumed as calibrated with SN Ia in Cao et al. (2017a). These functions (except the zero mean function) are shown in Figure 2. There are many possible covariance functions and we studied the three most popular ones. They comprise: the squared exponential function 8 the 9 and Cauchy covariance function 10 where σf and ℓ are hyperparameters that control the amplitude of deviation from the mean function and the typical length scale in x-direction, respectively. It is instructive to discuss the effects of the mean function and covariance function selection (prior assumptions) on the reconstruction. In order to show the impact of covariance function choice, we fixed the zero mean and preformed reconstruction with three different covariance functions mentioned above. The result obtained under the assumption of squared exponential covariance function is illustrated in Figure 4, where the green solid line represents the reconstructed θ(z) relation and the green region around it represents a 1σ uncertainty band. The blue dashed line and black dashed–dotted line represent the reconstructed θ(z) relation with the Matérn and Cauchy covariance functions, respectively. Their uncertainty bands are not shown in order to not blur the picture since they are similar to the one displayed. One can see that differences between reconstructions performed with different choices of covariance function are insignificant. Similarly, we checked sensitivity of reconstructions with respect to the choice of the mean function fixing the covariance as a squared exponential one and using three main functions mentioned above. It turned out that the impact of mean function choice on the reconstruction was even smaller than that of the covariance function. Therefore for further calculations we assumed the zero mean function and the squared exponential covariance function to get the reconstructed θ(z) function (i.e., the green line and region in Figure 4). Using this reconstructed relation we were able to have a one-to-one matching between the QSO [CRS] angular diameter distance and the QSO [XUV] luminosity distance at the same redshift.

2021AandA...649A..58L__Bemporad_et_al._(2018)_Instance_1

The leading edges of the transients normally leave bright traces in the images of visible light, inspiring many methods that were developed to derive their locations and velocities, such as the icecream cone model (Fisher & Munro 1984), the graduated cylindrical shell (GCS) model (Thernisien 2011), geometric triangulation methods (Liu et al. 2010), mask-fitting methods (Feng et al. 2012), and trace-fitting methods including the point-p, fixed-Φ, harmonic mean, and self-similar expansion fitting methods (e.g., Sheeley et al. 1999; Howard et al. 2006; Davies et al. 2012; Möstl & Davies 2013). To derive the velocity distribution inside one transient rather than only at its leading edge, some other techniques have been proposed. Colaninno & Vourlidas (2006) applied an optical flow tool to extract the velocity vector of a coronal mass ejection (CME) in digital images. Feng et al. (2015) derived the radial velocity profiles of the whole CME from the spatial distribution of its density given by the mass continuum equation. A cross-correlation method was applied to derive continuous 2D speed maps of a CME from coronagraphic images by Bemporad et al. (2018). In their work, the radial shift pixel by pixel is determined by maximizing the cross correlation between the signal in a radial window at one frame and the signal in a radial shifted window at the previous frame, and the radial speed just equals the radial shift over the time interval between the two frames. Ying et al. (2019) improved this cross-correlation method by analyzing data in three steps: forward step (FS), backward step (BS), and average step (AS). In the FS (BS), the 2D velocity map between the current and the previous (next) frame is constructed with almost the same method as Bemporad et al. (2018). In the AS the average, velocity is obtained from the FS and BS. The velocities derived by all these methods are the component of the flow velocity vector projected onto the POS. This may underestimate the velocity especially for transients that do not propagate in the POS. Methods such as the polarizaition ratio technique (Moran & Davila 2004; DeForest et al. 2017) or the local correlation tracking (LCT) method (Mierla et al. 2009) can derive the 3D geometric information of the whole transients, but not the velocity distribution. Bemporad et al. (2018) chose the propagating direction averaged over the whole CME derived by the polarization ratio technique to correct the radial speed in the 2D maps, but the key information along the LOS is still lacking.

2022MNRAS.516.5289M__Thompson_et_al._2015_Instance_1

Given the number densities within the mass-dissociation index plane of Fig. 8, we now ask ourselves whether known dissociated clusters, such as the Bullet cluster, are expected in L210N1024NR? The Bullet Cluster has a mass of $\sim 1.5 \times 10^{15} \, {\rm M}_{\odot }$ (e.g. Clowe et al. 2004; Bradač et al. 2006; Clowe et al. 2006) and we estimated a dissociation index of SBullet ∼ 0.335 ± 0.06. As seen in Fig. 8 there are no Bullet cluster analogues (structures of approximate mass and dissociation) in L210N1024NR, this is unsurprising as a simulation requires a significantly larger volume than that of L210N1024NR ((210cMpc h−1)3) to expect such an object (e.g. Lee & Komatsu 2010; Thompson & Nagamine 2012; Bouillot et al. 2015; Kraljic & Sarkar 2015; Thompson et al. 2015). From the distribution presented in Fig. 8, it is trivial to estimate the required cosmological volume (the effective volume, Veff) to expect structures of a given mass and dissociation index. By separating the 2D distribution on the mass-dissociation index planes into the component 1D distributions of mass and dissociation the effective volume is computed as (12)$$\begin{eqnarray} V_\text{eff}~^{-1} &=&\int \int \,{\rm{ d}} S \, {\rm{ d}} M \phi (S, M) \\ &=& \int _{S_\text{a}}^{S_\text{b}} \, {\rm{ d}} S \phi _S(S) \int _{M_\text{a}}^{M_\text{b}} \, {\rm{ d}} M \phi _M(M)~, \end{eqnarray}$$where ϕS(S) is the number density function associated with S and $\phi _\mathit {M}(\mathit {M})$ is the mass function presented in Fig. 7. Assuming a probable range of S = 0.335 ± 0.06 and $1 \lt M \lt 2 \times 10^{15} \, {\rm M}_{\odot }$ we estimate a number density ∼4.92 × 10−10 Mpc−3 or that an effective volume of ∼2.03 Gpc3 would be required to observe a single Bullet-like cluster. This result is inline with the number density estimate of the order of ∼10−10 Mpc−3 by Thompson et al. (2015), which improves on previous estimates (e.g. Lee & Komatsu 2010; Thompson & Nagamine 2012; Bouillot et al. 2015) due to more sophisticated halo finding methods (e.g. Behroozi, Wechsler & Wu 2013). Conversely, it was estimated by Kraljic & Sarkar (2015) (utilizing the same halo finder as Thompson et al. 2015) that given an effective volume of ∼14.6 Gpc3, no Bullet cluster analogues are expected, however as indicated by a pairwise velocity distribution it would be expected that present binary halo–halo orbits have the potential to form a Bullet-like object.

2021MNRAS.504..444C__Russell_et_al._2020b_Instance_1

To better visualize the jet behaviour in this phase, we show in Fig. 10 the radio light curve at the core location for the first 25 d of the outburst, taken from Fig. 2. The compact jet emission peaks on MJD 58520 and then starts to decay on MJD 58521, 1 d before the system enters in the soft state and 2 d after the inferred RK1 ejection date (see Section 3.3). This whole evolution is accompanied by a smooth transition between the optically thick and the optically thin regimes of synchrotron radio emission. Compact jets are usually observed to quench at the transition from the hard to soft state (e.g. Fender et al. 1999; Corbel et al. 2000). This phenomenon is observed to start at higher frequencies (where the emission is produced closer to the compact object) and terminates as the jet break evolves through the radio band (Russell et al. 2013b, 2014; Russell et al. 2020b). It is not yet clear if compact jets switch off before or during the launch of discrete ejections, and how (or if) the two events are linked (Russell et al. 2020a). We do not have a MeerKAT observation on MJD 58521, but, as the flat spectrum obtained with ATCA suggests, the flux density would have been at the ∼50 mJy level. The following observations show instead a quick rise in flux density and a subsequent decay until MJD 58531, when the source is steadily in the soft state. With our data, we cannot conclude on the origin of the radio emission on MJD 58521, which could be produced either from the compact jet that is quenching (with no evidence of the spectral break in the radio band), or by the first self-absorbed part of the radio flare observed to peak on MJD 58523. The latter scenario implies that the jet significantly quenched in less than 2 d, a shorter time-scale with respect to what observed for other sources (e.g. Russell et al. 2013b, 2020a). Therefore, we are not able to precisely order in time the ejection of RK1 and the quenching of the compact jets, and thus we cannot draw conclusions on a potential link between the two events.

2019MNRAS.488..902C__Svensson_et_al._2012_Instance_1

Long-duration gamma-ray bursts (GRBs) give rise to a synchrotron afterglow, detectable at optical wavelengths if sufficiently rapid and deep follow-up observations are made. A substantial fraction, however, lack such emission even when it would be expected from extrapolation of the X-ray spectral slope (Groot et al. 1998; Fynbo et al. 2001). When the X-ray to optical spectral slope, βOX, is below the recognized threshold of 0.5, the event is classified as ‘dark’ (Jakobsson et al. 2004). This is typically evaluated at 11 h post-burst to avoid contamination from early-time effects including X-ray flares and plateaus. An alternative method uses βOX βX − 0.5 to define darkness (van der Horst et al. 2009). There are two primary causes for darkness in GRBs: attenuation by dust, or rest frame ultraviolet H i absorption at high redshift (e.g. Fruchter 1999; Levan et al. 2006; Perley et al. 2009,2013; Greiner et al. 2011; Svensson et al. 2012; Zauderer et al. 2013; Chrimes et al. 2019; Higgins et al. 2019). The number of GRBs known at high-redshift (z > 5, in the epoch of reionization) is small (∼15, from around 500 GRBs with a known or estimated redshift, Cenko et al. 2006; Grazian et al. 2006; Jakobsson et al. 2006; Kawai et al. 2006; Ruiz-Velasco et al. 2007; Salvaterra et al. 2009; Greiner et al. 2009; Tanvir et al. 2009; Cucchiara et al. 2011; Afonso et al. 2011; Castro-Tirado et al. 2013; Laskar et al. 2014; Jeong et al. 2014b; Chornock, Fox & Berger 2014b; Tanvir et al. 2018), and each one is valuable, as they provide insight into star formation in the low mass, low luminosity galaxies which power the epoch of reionization. Because they have small projected offsets from their hosts, high-redshift GRBs with a detected afterglow uniquely allow us to place accurate, deep upper limits on the luminosities of the faintest, undetected galaxies, probing fainter galaxies than deep field studies (Berger et al. 2007; Tanvir et al. 2012; Trenti et al. 2012; McGuire et al. 2016). For those with the brightest afterglows, insight into the burst environment can be gained from absorption lines in their spectra (e.g. Kawai et al. 2006; Chornock et al. 2014a; Sparre et al. 2014; Hartoog et al. 2015).

2015ApJ...807...92Y__Yi_et_al._2013_Instance_1

The assumptions of the equilibrium of pressures and equality of velocities along the contact discontinuity lead to and , respectively. With the jump condition for the shocks and the equilibrium of pressures, we can obtain 1 The Lorentz factor of the reverse shock could be approximated as 2 as long as and . Substituting Equation (2) into (1), we can obtain the following equation: 3 Because , , and , we ignore the constant 1/2 term in Equation (3) and thus we can obtain the solution of this equation (ignoring the negative solution, also see Panaitescu & Kumar 2004), 4 Here we obtain the relation between the Lorentz factor of the shocked fireball shell and the initial Lorentz factor ( ), which depends on the ratio of these two comoving densities. The number density of the ambient medium is assumed to be (Dai & Lu 1998; Mészáros et al. 1998; Chevalier & Li 2000; Wu et al. 2003, 2005; Yi et al. 2013); such a circumburst medium is a homogeneous ISM for k = 0, and a typical stellar wind environment for k = 2. The fireball shell is characterized by an initial kinetic energy Ek, initial Lorentz factor , and a width Δ in the lab frame attached to the explosion center, so the number density of the shell in the comoving frame is . The ratio of the comoving number density of the relativistic shell to the number density of the ambient medium defined in Sari & Piran (1995) is 5 where . The difference between the lab frame speed of the unshocked fireball shell and that of the reverse shock is (Kumar & Panaitescu 2003), 6 Considering the thin shell case , we can calculate the radius where the reverse shock finishes crossing the fireball shell, 7 The substitution of Equations (5) and (6) into (7) leads to 8 So the comoving density ratio at is 9 Substituting Equation (9) into (4), we can obtain the Lorentz factor of the reverse shock as it finishes crossing the shell: 10 Therefore, the relation between and the initial Lorentz factor is 11 and 12 For the thin shell case, the reverse shock crossing time almost corresponds to the deceleration time Tdec, i.e., . Therefore, we can derive the initial Lorentz factor in the ISM and wind-type cases (also see Panaitescu & Kumar 2004). For k = 0 (ISM), 13 and for k = 2 (wind), 14 With the isotropic-equivalent energy and the peak time of the afterglow onset , we can estimate the initial Lorentz factor of GRBs, where . Liang et al. (2010) discovered a tight correlation between and using 20 GRBs which show a deceleration feature in the early afterglow light curves. Other work also confirmed this correlation, but with different methods and power-law indices (Ghirlanda et al. 2012; Lü et al. 2012). Using the data on and from Liang et al. (2010, 2013) and Lü et al. (2012), we re-constrain the initial Lorentz factor, and also discover a tight and correlation for the ISM and wind cases. The and correlation in the wind case is even tighter than that in the ISM case, as shown in Figures 4 and 5.

2020AandA...637A..82D__Mason_et_al._2004_Instance_1

The emission in the diffuse interstellar medium of these bands, dominated by an aromatic vibrational character, also called AIBs (aromatic infrared bands), has led to the so-called polycyclic aromatic hydrocarbon (PAH) hypothesis. Under this theory, the observed emission is related to the infrared fluorescence emission mechanism of PAH-like molecules (Leger & Puget 1984; Allamandola et al. 1985), following energetic photon absorption, although no unique PAH has been identified in the mid-infrared so far. Apart from the recent attribution of a few specific infrared emission bands to the C60 and possibly C70 fullerene molecules in some sources (Sellgren et al. 2009, 2010; Cami et al. 2010), the carriers of the AIBsremain elusive. The emission bands have been categorised in different classes ( $\mathcal{A}$ A to $\mathcal{D}$D ) following the ascertainment of band profiles and center position variations, ensuing from a phenomenological deconvolution of the observations. (Peeters et al. 2002; van Diedenhoven et al. 2004; Matsuura et al. 2007; Sloan et al. 2007; Keller et al. 2008; Boersma et al. 2008; Pino et al. 2008; Acke et al. 2010; Carpentier et al. 2012; Gadallah et al. 2013). In the late classes of infrared emission spectra (so-called $\mathcal{C}$C and $\mathcal{D}$D ), a mix between an aromatic and aliphatic character is observed. Class $\mathcal{A}$A sources are dominated by aromatic bands, whereas classes $\mathcal{C}$C and $\mathcal{D}$D harboura pronounced aliphatic character. Most of the interstellar hydrocarbons are injected through the late phases of stellar evolution, post asymptotic giant branch (AGB) and protoplanetary nebula (PPN), which often display the class $\mathcal{C}$C and $\mathcal{D}$D spectral character, and are eventually processed later in the ISM. In absorption, bands at 3.4, 6.85, and 7.25 μm are also observed in the diffuse interstellar medium (ISM) of our Galaxy, as well as in extragalactic ISM, and can be well represented by a material with a significant amount of aliphatic character, also called HAC or a-C:H, which is a family of hydrogenated amorphous carbons (e.g. Allen & Wickramasinghe 1981; Duley & Williams 1983; Mason et al. 2004; Risaliti et al. 2006; Dartois et al. 2007; Dartois &Muñoz-Caro 2007; Imanishi et al. 2010, and references therein). However, the AIB emission spectra are not a simple linear combination of aliphatic (such as the a-C:H observed in absorption in the diffuse ISM) and aromatic (class $\mathcal{A}$A observed in emission) spectra. They show a spectral evolution of vibrational modes, including the shift of the C=C mode from 6.2 to 6.3 μm from class $\mathcal{A}$A to $\mathcal{C}$C (e.g. van Diedenhoven et al. 2004), and broadening and shifting back to lower wavelengths in class $\mathcal{D}$D . Unlike classes $\mathcal{A-B}$A−B with two bands in the 7.6 to 8.2 μm range, classes $\mathcal{C-D}$C−D show a broad band (Szczerba et al. 2005; Matsuura et al. 2014), with the class $\mathcal{D}$D one peaking at a shorter wavelength. The out of plane, predominantly aromatic, CH vibration patterns (from 11 to 13 μm) are generally difficult to reproduce with dust analogues in the laboratory. In addition to chemically pure PAH studies, a large range of dust grain analogues have been tailored using lasers, flames, VUV continuum sources, and/or plasmas in the laboratory to tackle this identification issue (Carpentier et al. 2012; Schnaiter et al. 1999; Jäger et al. 2006; Mennella et al. 1999; Dartois et al. 2005; Furton et al. 1999; Biennier et al. 2009). Among the issues faced by laboratory synthesis of interstellar dust analogues is the ability to produce an environment that is not homogeneous for the entire batch of analogues produced (i.e. localised modifications), as many processes in the interstellar medium will only affect grain (surfaces) moieties considering, for example, hydrogen or radical accretion/addition, cosmic ray impact, grain-grain shocks, etc. As a consequence, a large inhomogeneity at the nanometre scale between adjacent constitutive elements making the grain may be expected, introducing many local defects that will influence the nature and spectroscopic properties of such small dust grains. In this study, we address a new non-homogeneous, non-bottom-up shock approach. We used a mechanochemical synthesis method under a pressurised hydrogen atmosphere, to produce laboratory interstellar dust grain analogues to explain the infrared emission spectra of the remotely observed aliphatic and aromatic mixed interstellar dust grains observed through infrared emission bands.

2015MNRAS.450.3458C__Cichowolski_et_al._2001_Instance_6

The kinetic energy stored in the CO shell can be estimated as $E_{\rm kin} = 0.5\, M_{\rm shell}\, V^2_{\rm exp}$, where Vexp is the expansion velocity of the shell and Mshell is the total (molecular, atomic, and ionized) shell mass. Adopting an expansion velocity equal to half the velocity interval where the structure is observed, Vexp = 7.0 ± 1.3 km s− 1 , the molecular mass given in Table 1 and the atomic and ionized masses estimated by Cichowolski et al. (2001), 1450 and 3000 M⊙, respectively, we obtain Ekin = (2.5 ± 1.0) × 1049 erg, assuming a 40 per cent error for the masses.. Although Cichowolski et al. (2001) concluded that WR 130 could have alone created the observed structure, it is important to note that they did not take into account the molecular mass present in the shell, which considerably increases the kinetic shell energy. Thus, we can compare now the new value obtained for Ekin with the mechanical energy deposited in the ISM by the wind of the WR star, Ew = (0.7–2.2) × 1050 erg (Cichowolski et al. 2001). We obtain ϵ = Ekin/Ew = 0.007–0.5. The ratio ϵ measures the energy conversion efficiency in the shell, and according to evolutionary models ϵ ≤ 0.2 (Koo & McKee 1992). Thus, not all the possible values of ϵ are compatible with the scenario where the energy injected during the WR phase is enough to create the structure. In this case, the contribution of the energy injected during the O-star phase and/or other massive stars, should be considered. As mentioned in the Introduction, WR 130 is a WNH star, and according to Smith & Conti (2008) its age would be of about 2–3 Myr and its initial mass of at least 60 M⊙. A rough estimation of the energy injected by such a star during its main sequence yields Ew = (2.5–3.5) × 1050 erg (de Jager, Nieuwenhuijzen & van der Hucht 1988), which would be enough to create the observed structure. We have nevertheless looked for the presence of other massive stars in the region. We queried the available catalogues such as the Galactic O-Star Catalog (Maíz Apellániz et al. 2013), the Early-Type Emission-Line Stars Catalogue (Wackerling 1970), the Catalogue of Be stars (Jaschek & Egret 1982), the H-alpha Stars in the Northern Milky Way Catalogue (Kohoutek & Wehmeyer 1997), and the Catalog of Galactic OB Stars (Reed 2003), for early-type and emission stars. No stars were found in any catalogue. The only massive star located nearby is, as mentioned by Cichowolski et al. (2001), an OB star, which has an uncertain spectral type and no distance estimate (Stock, Nassau & Stephenson 1960). It is located in projection not in the centre of the structure but on to the shell (there is a second OB star mentioned by Cichowolski et al. 2001 but its location is actually outside the structure, see fig. 1 of Cichowolski et al. 2001). Although we cannot completely rule out the possibility that the OB star may be playing a role in creating the shell structure, we think that the action of WR 130 is sufficient and most likely dominant in the region.

2016MNRAS.457..875P__Kotov,_Churazov_&_Gilfanov_2001_Instance_1

After the discovery of hard X-ray lag relative to soft X-rays in binary system (e.g. Miyamoto et al. 1988; Nowak et al. 1999), similar lags were also observed in AGN (Papadakis, Nandra & Kazanas 2001; McHardy et al. 2004). The origin of hard lag is not clearly known. One possible explanation is provided by the propagation fluctuation model in which fluctuations associated with accretion flow propagate inwards in an accretion disc and thus resulting in the emission of the soft photons from relatively outer regions earlier than the hard photons from the innermost regions (Lyubarskii 1997; Kotov, Churazov & Gilfanov 2001; Arévalo & Uttley 2006). Recently, for example, Swift monitoring of the radio-loud NLS1 galaxy PKS 0558−504 for ${\sim } 1.5{\rm \ {\rm yr}}$ has revealed that optical leads UV and UV leads soft X-rays on short time-scales of about a week (Gliozzi et al. 2013) possibly favouring the propagation model. A new type of lag has emerged from recent studies where soft photons lag to the hard photons. This is termed as the reverberation lag which is used to constrain the X-ray emitting region in AGN. Fabian et al. (2009) discovered the reverberation lag ∼ 30 s for the first time in a NLS1 galaxy 1H0707−495. Since then, such lags have been observed in dozen of Seyfert galaxies (Zoghbi et al. 2010; de Marco et al. 2011; Emmanoulopoulos, McHardy & Papadakis 2011b; Zoghbi & Fabian 2011; Zoghbi, Uttley & Fabian 2011; Cackett et al. 2013; De Marco et al. 2013; Fabian et al. 2013; Kara et al. 2013). The most of above cases reveal reverberation lag ∼100 s supporting the compact nature of X-ray-emitting region within few gravitational radii of a supermassive black hole (SMBH). In case of strong illumination, such as that implied by observation of strong blurred reflection, UV/optical emission from AGN may be dominated by the reprocessed emission and the variations in the optical/UV band emission lag behind the X-rays (e.g. McHardy et al. 2014). About a five year long campaign of Seyfert 1 galaxy Mrk 79 using six ground-based observatories for optical and RXTE for X-ray observations, Breedt et al. (2009) have shown zero lag between optical and X-rays on time-scale of about a day. Their study of correlated X-ray and optical emission suggests X-ray reprocessing on short time-scale of days and the changes in the optical emission on long time-scale of ∼ few years can be attributed to the variations in the accretion rate.

2021MNRAS.503..354G__Hou_&_Han_2014_Instance_1

The spatial distribution of OB stars and associations, young long-period Cepheids and open clusters, star-forming regions, H ii regions, interstellar dust, and giant molecular and neutral gas clouds in the solar vicinity that have been in existence generally τ ≲ 108 yr is known to correlate with the location of the inner Sagittarius, the closest Orion, and outer Perseus spiral arm segments. (The distances for the vast majority of these spiral tracers have been determined in the literature with trigonometric or photometric methods.) The Sun is situated at the inner edge of the Orion arm (Levine et al. 2006; Hou & Han 2014; Nakanishi & Sofue 2016; Xu et al. 2018, 2021; Lallement et al. 2019; Reid et al. 2019; Skowron et al. 2019; Cantat-Gaudin et al. 2020; Fig. 2 above).3 These three spatial features nearby to the Sun appear to form part of the global spiral structure in the Galaxy. Contrary, the objects of older population with larger random velocities, for instance, main-sequence A–K stars or the oldest Cepheids and open clusters, do not currently follow the exact location of those arms (e.g. Cantat-Gaudin et al. 2020, fig. 8 therein; Griv et al. 2020, fig. 7 therein). The latter can be explained by the difference in rotation velocity between the spiral density waves and the objects. Investigating the velocity field of Xu et al.’s (2018) O and early B-type stars in the framework of the Lin–Shu density-wave proposal, we also found that the Sun lies within the Orion arm, at the inner edge of this spiral. The radial distance from the Sun to the centre of the Orion arm is ≈0.2 kpc in the direction of the Galactic anticentre, the centre of the Sagittarius arm is ≈1.8 kpc from the Sun in the direction of the GC, and the width of the arms is ≈0.5 kpc. The radial distance between the centres of the Orion and Sagittarius arms near the Sun is λrad ≈ 2 kpc (cf. Hou & Han 2014; Wu et al. 2014; Bovy et al. 2015). As for us, the nearest Orion spiral arm forms part of the dominant density-wave structure of the system.

2018MNRAS.480..927P__Richardson_&_Fairbairn_2014_Instance_1

The core/cusp problem is a clear example of this controversy: on the one hand, cosmological dark matter only N-body simulations predict cuspy dark halo density profiles; on the other hand, the rotation curves of low surface brightness disc and gas-rich dwarf galaxies favour shallower or cored dark matter density distributions (de Blok 2010 and references therein). Also for dSphs, for which the determination of the dark matter density distribution is more difficult, there are indications that cored dark matter density profiles may be favoured with respect to cuspy profiles (Kleyna et al. 2003; Goerdt et al. 2006, Battaglia et al. 2008; Walker & Peñarrubia 2011; Salucci et al. 2012; Amorisco, Agnello & Evans 2013; Zhu et al. 2016), though this finding is still debated (Richardson & Fairbairn 2014; Strigari, Frenk & White 2017). It must be stressed, however, that cored dark haloes in dSphs do not necessarily imply a failure of ΛCDM: dark matter only cosmological simulations may not reliably predict the present-day dark matter distribution in dSphs because, by definition, they neglect the effects of baryons on the dark haloes. Even in a galaxy that is everywhere dark matter dominated today, baryons must have been locally dominant in the past to permit star formation. Therefore, the effect of baryon physics on the dark halo is expected to be important also in dSphs. For instance, Nipoti & Binney (2015) showed how, due to the fragmentation of a disc in cuspy dark halo, dynamical friction may cause the halo to flatten the original cusp into a core even before the formation of the first stars (see also El-Zant, Shlosman & Hoffman 2001; Mo & Mao 2004; Goerdt et al. 2010; Cole, Dehnen & Wilkinson 2011; Arca-Sedda & Capuzzo-Dolcetta 2017). Moreover, the results of hydrodynamical simulations suggest that, following star formation, supernova feedback can also help to flatten the central dark matter distribution, by expelling the gas (Navarro, Eke & Frenk 1996a; Read & Gilmore 2005) and thus inducing rapid fluctuations in the gravitational potential (Mashchenko, Couchman & Wadsley 2006, Pontzen & Governato 2012, Tollet et al. 2016).

2019MNRAS.485.4343C__Rodriguez-Bernal_2012_Instance_1

We obtain computationally credible samplings of the posterior probability (equation 8) by removing the burn-in steps of the random walk according to the autocorrelation time. We can then create synthetic data sets by drawing a parameter sample $\pmb {\theta }_k$ from the posterior and using it to draw from the likelihood to create a new data set, i.e. drawing new σDj from the probability distribution for all galaxies in the original data set using equation (6). We then assess the validity of the model by comparing synthetic data with the observed (i.e. original) data. This comparison is done by using a discrepancy measure $\mathcal {D}(\sigma _\mathrm{ D}|\pmb {\theta }_k)$ between data and model-derived expected values for the same data $e=\lbrace e_j(\pmb {\theta }_k)\rbrace$, where $\boldsymbol{\theta}_k$ is drawn from the posterior distribution and σD can be the observed errors or the model-generated synthetic errors. The discrepancy can be calculated using a statistic like χ2 (de la Horra 2008; de la Horra & Teresa Rodriguez-Bernal 2012), but here we will work with the Freeman–Tukey discrepancy since it is weight independent (Brooks, Catchpole & Morgan 2000; Bishop, Fienberg & Holland 2007), \begin{eqnarray*} \mathcal {D}(\sigma _\mathrm{ D}|\pmb {\theta }_k)=\sum _j^m \left(\sqrt{\sigma _{\mathrm{ D}j}\vphantom{e_j(\pmb {\theta }_k)}}-\sqrt{e_j(\pmb {\theta }_k)}\right)^2. \end{eqnarray*} For each parameter draw k, it is possible to compare the simulated discrepancy with the observed discrepancy. If the model is representative of the data, then for many parameter draws, the simulated and observed discrepancies should be similar. We can then calculate a Bayesian ‘p-value’ as the ratio of ‘draws when the observed discrepancies are larger than the synthetic discrepancies’ to ‘total draws’. If this Bayesian p-value is too close to 0 or to 1 we can reject the model, otherwise it is generating synthetic data that are similar to the original data. This is better visualized using a discrepancy plot, where for each draw k, a synthetic discrepancy is paired with its corresponding observed discrepancy. If the discrepancy points are roughly equally distributed about the $\mathcal {D}_\mathrm{obs}=\mathcal {D}_\mathrm{sim}$ line, then we cannot reject the model. As mentioned above, we expect that galaxies with the largest number of measurements are sampling more completely the ‘true’ distribution of the distance. Therefore, we need to find the minimum number of measurements per galaxy for which the Bayesian p-value shows an agreement between on the partitioned data set and the model predictions.

2021MNRAS.507.4564P__Murgia_2003_Instance_1

For the radio galaxies in our sample, we estimate the minimum jet power, Pj following Wójtowicz et al. (2020): (4)$$\begin{eqnarray} P_{\rm j} &\sim & 1.5 \times 10^{45} \times \left(\frac{{\rm LS}}{{\rm 100 \, pc}} \right)^{9/7} \left(\frac{\tau _{\rm j}}{{\rm 100 \, yr}} \right)^{-1} \\ &&\times \, \left(\frac{L_{\rm 5 \, GHz}}{10^{42} \,{\rm erg \, s^{-1}}} \right)^{4/7} {\rm erg\, s^{-1}} , \end{eqnarray}$$where LS is the linear size, τj is the source age, and L$_{\rm 5 \, GHz}$ is the luminosity at 5 GHz. In equation (4), we consider the linear size and the luminosity at 5 GHz reported in Tables A1–A3. We assume ages between about 100 yr, for the most compact sources, and 105 yr for sources with LS of several kpc, as derived from radiative and kinematic ages of sources (e.g. Murgia et al. 1999; Fanti & Fanti 2002; Murgia 2003; Polatidis & Conway 2003; Giroletti & Polatidis 2009). We end up with minimum jet powers for galaxies between 1040 and 1046 erg s−1. The higher values are obtained for sources at higher redshift, and for 3C 346, which is among the marginally detected sources from our analysis. However, the majority of the galaxies have L$_{\rm \, 5 GHz} \lt 10^{43}$ erg s−1 and estimated minimum jet power Pj 1044 erg s−1. As shown in Fig. 11, this requires a UV luminosity above 1045 erg s−1 in order to detect a cumulative signal by the stacking analysis. This is far from the expectation from Stawarz et al. (2008), in which the optimal conditions occurred for sources with jet power ∼1046 erg s−1, LS 100 pc, and at redshift 0.2 (∼1 Gpc). For our estimated jet power, the highest expected gamma-ray luminosity for sources with LS 100 pc is 1044 erg s−1. The fact that young radio galaxies are faint emitters of gamma rays is also suggested by the results of the stacking analysis, which set the upper limit to their emission, as a whole population, an order of magnitude below the Fermi-LAT threshold. This indicates that only the closest sources could be detected by Fermi-LAT, while if we consider objects at higher and higher redshift, boosting effects are necessary for their detection.

2020MNRAS.493...87T__Maraston_et_al._2013_Instance_1

The other significant source of scatter in the size–mass plane is the uncertainty in measuring the total stellar mass of galaxies from the integrated stellar mass density profile of the objects. As explained in Section 5.4, we measure our total stellar mass by integrating the stellar mass density profiles. To quantify how the uncertainty in the total stellar mass affects our results, we have assumed the following uncertainties in measuring the stellar mass: δmass = 0.24 ± 0.01 dex (for the entire sample), δmass = 0.19 ± 0.01 dex (for the E0-S0 + subsample), δmass = 0.24 ± 0.01 dex (for the S0/a-Sm subsample) and δmass = 0.25 ± 0.03 dex (for the Dwarfs subsample). These values were computed by an analysis of the differences between the Portsmouth stellar masses of our galaxies (Maraston et al. 2013) and those we measured using the g–r colour profile (Roediger & Courteau 2015, see Appendix D for further details). To model the effect of the mass uncertainty ($\sigma _{\rm R_{mass}}$) on the scatter of the scaling relationship, all the observed stellar mass profiles were either scaled up or down in mass to place the galaxies on the best-fitting line through the observed stellar mass plane. This has been performed self-consistently, i.e. taking into account the change in the location of R1 due to the scaling of the profile. Once all the galaxies are located exactly on top of the best fitting stellar mass–size relation (i.e. with zero scatter), we randomly scale the stellar mass density profiles up or down again, this time by a quantity compatible with a Gaussian distribution whose standard deviation is given by the above δmass values. We repeat this procedure 1000 times and on each occasion we measure the scatter of the stellar mass–size plane produced by the uncertainty in measuring the stellar mass. We show an illustration of the scatter of the stellar mass–size relation caused by the uncertainty in stellar mass in Fig. D2. The scatter in the stellar mass–size plane generated by the uncertainty in mass is shown in Table 3. Interestingly, for R1, RH, and R23.5, i, we find that the dwarfs are the most affected by the uncertainty due to our mass determination. This is once again expected as the star formation activity of dwarf galaxies is, on average, more stochastic (Kauffmann 2014) and complicated to model than that of massive spirals and ellipticals. Therefore, a single colour is not a good proxy for the M/L ratio of dwarfs as it is in the case for more gentle star formation histories.

2019MNRAS.482.5430B__Eerten_&_MacFadyen_2012_Instance_1

In light of this, the allowed structure of gamma-ray burst (GRB) jets and the efficiency at which it produces gamma-rays at large angles remains a topic of major importance, and it is useful to consider what types of jet structures are consistent with GRB observations (see also Beniamini et al. 2018b). Previous studies have considered the implications of structure models on the true energetics and rates of GRBs (Frail et al. 2001; Lipunov, Postnov & Prokhorov 2001; Rossi, Lazzati & Rees 2002; Zhang & Mészáros 2002; Eichler & Levinson 2004; van Eerten & MacFadyen 2012; Pescalli et al. 2015), on the shape of the afterglow light curve (Granot & Kumar 2003; Kumar & Granot 2003; Salmonson 2003) or on detectability of orphan afterglows (Lamb & Kobayashi 2017). Here, we propose a novel way to test the allowed structure of GRBs (in terms of both the energy and Lorentz factor angular distributions), by applying three independent techniques. We focus on long GRBs for which more detailed observations are available. First, we compare the predictions of these models regarding the EX/Eγ distribution (i.e. the isotropic equivalent early X-ray afterglow to prompt gamma-ray energy ratio) to the observations. We show that a variety of structure models predict large variations in this quantity, in contrast with results from GRB observations. Secondly, we reconsider the effect of the structure on the observed luminosity function and show that a large family of models can be ruled out as they lead to an overproduction of bursts with gamma-ray luminosities below the peak of the observed luminosity function. Both these considerations imply that while the energy angular profile may be steep, the Lorentz factor of GRBs must remain large at any region that produces gamma-rays efficiently. However, even such models typically lead to very peculiar light curves that can be ruled out by observations. The most likely implication is that efficient gamma-ray emission must be confined to a narrow opening angle around the jet’s core, where the isotropic equivalent energy is not much lower than that of the core. This will naturally resolve all the problems mentioned above.

2021MNRAS.505.5833F__Crocce_&_Scoccimarro_2006_Instance_1

Besides the Patchy and the LN mocks, we also model the multipoles of the BOSS CMASS two-point correlation function using an analytic approach, which is required to run the Monte Carlo analysis (see Section 5). The 2PCF can be obtained from the Fourier transform of the matter power spectrum, P(k), for which we assume the template from Padmanabhan & White (2008): (10)$$\begin{eqnarray*} P(k)=\left[P_{\rm {lin}}(k)-P_{\rm {dw}}(k)\right]e^{-k^2\Sigma _{\rm {nl}}^2/2}+P_{\rm {dw}}(k) . \end{eqnarray*}$$In the equation above, Plin(k) is the linear matter power spectrum computed using the Boltzmann code CLASS (Lesgourgues 2011), assuming the Planck 2015 (Ade et al. 2016) fiducial cosmology. The Pdw(k) term is the de-wiggled power spectrum (Eisenstein & Hu 1998), while the Σnl parameter encodes the smoothing of the BAO peak due to non-linear effects (Crocce & Scoccimarro 2006). The multipoles of the analytic 2PCF are defined as (11)$$\begin{eqnarray*} \xi _l(s) = \frac{i^l}{2\pi ^2}\int _0^{\infty } P_l(k)j_l(ks)k^2{\rm d}k , \end{eqnarray*}$$from which we recover the monopole (l = 0) and the quadrupole (l = 2). In equation (11), jl(x) represents the spherical Bessel function of first kind and order l, while Pl(k) are the multipoles of the power spectrum defined as (12)$$\begin{eqnarray*} P_l(k)=\frac{2l+1}{2}\int ^1_{-1}\left(1+f\mu ^2\right)^2P(k)L_l(\mu){\rm d}\mu , \end{eqnarray*}$$where Ll(x) is the Legendre polynomial of order l and P(k) is the template given in equation (10). By replacing equation (12) in equation (11), the analytic expressions for monopole (l = 0) and quadrupole (l = 2) are respectively (Xu et al. 2012): (13)$$\begin{eqnarray*} \xi _{\rm {model}}^{(0)}(s) = B_0\xi _0(\alpha s)+a_0^{(0)}+\frac{a_1^{(0)}}{s}+\frac{a_2^{(0)}}{s^2} , \end{eqnarray*}$$(14)$$\begin{eqnarray*} \xi _{\rm {model}}^{(2)}(s) = B_2\xi _2(\alpha s)+a_0^{(2)}+\frac{a_1^{(2)}}{s}+\frac{a_2^{(2)}}{s^2} , \end{eqnarray*}$$where α is the shift parameter, while $(a_1^{(i)},a_2^{(i)},a_3^{(i)})$ are linear nuisance parameters.

2019ApJ...875...90L__Li_et_al._2018a_Instance_1

When energy flows from the interior of the Sun outward into the solar atmosphere, why is the Sun’s outer atmosphere, the corona, much hotter than the inner atmosphere, the underlying chromosphere and photosphere? This is the long-standing problem of the coronal heating, which is one of the eight key mysteries in modern astronomy (Kerr 2012). For about 80 yr since the discovery of the extremely hot corona around the late 1930s (Grotian 1939; Edlen 1945), people have worked hard on addressing this issue, and great advances have been made in observation and theoretical studies (Parnell & De Moortel 2012; Amari et al. 2015; Arregui 2015; Cargill et al. 2015; De Moortel & Browning 2015; Jess et al. 2015; Klimchuk 2015; Longcope & Tarr 2015; Peter 2015; Schmelz & Winebarger 2015; Velli et al. 2015; Wilmot-Smith 2015). Especially during recent decades, high-resolution observations of solar super-fine structures indicate that small spicules, minor hot jets along small-scale magnetic channels from the low atmosphere upwards to the corona, petty tornados and cyclones, and small explosive phenomena such as mini-filament eruptions and micro- and nano-flares—all of these small-scale magnetic activities contribute greatly to coronal heating (De Pontieu et al. 2011; 2018; Zhang & Liu 2011; Parnell & De Moortel 2012; Klimchuk 2015; Peter 2015; Schmelz & Winebarger 2015; Henriques et al. 2016; Li et al. 2018a). Additionally, contributions of MHD waves to heating the corona have been observationally illustrated (van Ballegooijen et al. 2011; Jess et al. 2015; Kubo et al. 2016; Morton et al. 2016; Soler et al. 2017; Morgan & Hutton 2018). Meanwhile, with the progress of observational studies, two groups of theoretical models, magnetic reconnection models and magnetohydrodynamic wave models, have traditionally attempted to explain coronal heating, but so far no models can address the key mystery perfectly (van Ballegooijen et al. 2011; Arregui 2015; Cargill et al. 2015; Peter 2015; Velli et al. 2015; Wilmot-Smith 2015). Maybe we do not need to intentionally take to heart such the classical dichotomy, because waves and reconnections may interact with each other (De Moortel & Browning 2015; Velli et al. 2015). Additionally, statistical studies may look at coronal heating from a comprehensive perspective. Li et al. (2018b) found that the long-term variation of the heated corona, which is represented by coronal spectral irradiances, and that of small-scale magnetic activity are in lockstep, indicating that the corona should statistically be effectively heated by small-scale magnetic activity. Observational and theoretical model studies through heating channels and modes, and statistical studies by means of heating effect (performance of the heated corona), both suggest that coronal heating originates from small-scale magnetic activity.

2018ApJ...854..155K__Mei_et_al._2012_Instance_1

From ∼17:07 UT onward, especially during 17:12–17:14 UT, we detected multiple blobs in the bright, inverted-V-shaped structure below the flux rope, along with the fast rise of the filament (Figure 3). In Figure 12(b), boxes U and D encompass the upward- and downward-moving blobs, whose projected speeds are ∼135 and 55 km s−1, respectively. Some blobs also appear to coalesce during their propagation. We attribute the growing linear features beneath the rising flux rope to plasma emission associated with a current sheet, analogous to the flare current sheet in CME/eruptive flare models (e.g., Karpen et al. 2012). In this case, the multiple bright blobs are plasmoids formed by bursty reconnection in this current sheet, another phenomenon commonly found in high-Lundquist-number reconnection simulations (e.g., Daughton et al. 2006, 2014; Drake et al. 2006; Fermo et al. 2010; Uzdensky et al. 2010; Huang & Bhattacharjee 2012; Karpen et al. 2012; Mei et al. 2012; Cassak & Drake 2013; Guo et al. 2013; Wyper & Pontin 2014a, 2014b; Guidoni et al. 2016; Lynch et al. 2016). Multiple plasmoids moving bidirectionally were previously detected below flux ropes in active-region eruptive flares (Takasao et al. 2012; Kumar & Cho 2013; Kumar et al. 2015). If we assume a minimum base field strength of 50 G and an Alfvén speed of ∼135 km s−1 for an upward-moving plasmoid, we obtain an estimated minimum density of 4.5 × 1010 cm−3 for the flare current sheet. The curious appearance of the bright inverted-V-shaped structure diverging beneath the flux rope (see Figure 3 red and white arrows, Column 3, and the accompanying movie) underscores the 3D geometry of the flare current sheet. Here the right-hand bright line (marked by white arrows) appeared first (∼17:05 UT), followed by the left one (marked by a green arrow) at ∼17:13 UT. The right-hand line disappeared by ∼17:15 UT, while the left faded gradually through the rest of the observing period. A large downward-moving blob is visible during ∼17:17–17:18 UT. Because current sheets are very thin, they become visible only if the line of sight passes through multiple folds or through regions of enhanced density. We speculate that the appearance of two plasmoid-generating regions could be a sign of patchy reconnection in a rippled current sheet, with reconnection sites appearing at different locations along the sheet.

2015ApJ...799..149J___2014_Instance_6

With our joint analysis of stellar mass fraction and source size, we find a larger stellar mass fraction than earlier statistical studies. In Figure 2, we compare our determination of the stellar surface density fraction to a simple theoretical model and to the best fit of a sample of lens galaxies by Oguri et al. (2014). The simple theoretical model is the early-type galaxy equivalent of a maximal disk model for spirals. We follow the rotation curve of a de Vaucouleurs component for the stars outward in radius until it reaches its maximum and then simply extend it as a flat rotation curve to become a singular isothermal sphere (SIS) at large radius (see details in the Appendix). The ratio of the surface mass density of the de Vaucouleurs component to the total surface mass density is shown as a dashed curve in Figure 2. We also show as a gray band the best fit for the stellar fraction in the form of stars determined by Oguri et al (2014) in a study of a large sample of lens galaxies using strong lensing and photometry, as well as the best model using a Hernquist component for the stars and an NFW halo for the dark matter with and without adiabatic contraction, also from Oguri et al. (2014). We have used the average and dispersion estimates for the Einstein and effective radii available for 13 of the objects in our sample from Oguri et al. (2014), Sluse et al. (2012), Fadely et al. (2010), and Lehár et al. (2000; see Table 1) as an estimate of RE/Reff in Figure 2. The average value and dispersion of the sample is RE/Reff = 1.8 ± 0.8. This also averages over the different radii of the lensed images. The agreement of our estimates with the expectations of the simple theoretical model and with estimates from other studies (Oguri et al. 2014) is quite good. For comparison, the estimate of Pooley et al. (2012; using the Einstein and effective radii estimates for 10 out of 14 of their objects from Schechter et al. 2014) seems somewhat lower than expected at those radii. The range of stellar mass fractions from MED09 for source sizes in the range 0.3–15.6 light days is also shown in Figure 2. In this case, the discrepancy between our estimate and their reported value of α = 0.05 is completely due to the effect of the source size. Although accretion disk sizes are known to be smaller in X-rays, recent estimates are in the range of 0.1–1 light-days, depending on the mass of the black hole (see Mosquera et al. 2013), and these finite sizes will increase the stellar surface densities implied by the X-ray data. Another possible origin for this discrepancy is that Pooley et al. (2012) use the macro model as an unmicrolensed baseline for their analysis. It is well known that simple macro models are good at reproducing the positions of images, but have difficulty reproducing the flux ratios of images due to a range of effects beyond microlensing. Recently, Schechter et al. (2014) found that the fundamental plane stellar mass densities have to be scaled up by a factor 1.23 in order to be compatible with microlensing in X-rays in a sample of lenses with a large overlap with that analyzed by Pooley et al. (2012). It is unclear how this need for more mass in stars at the position of the images found by Schechter et al. (2014) can be reconciled with the apparently low estimate of mass in stars at those radii by Pooley et al. (2012). Our estimate of the stellar mass fraction agrees better with the results of microlensing studies of individual lenses (Keeton et al. 2006; Kochanek et al. 2006; Morgan et al. 2008, 2012; Chartas et al. 2009; Pooley et al. 2009; Dai et al. 2010) that reported values in the range 8%–25%, and with the estimates from strong lensing studies (see for example Jiang & Kochanek 2007; Gavazzi et al. 2007; Treu 2010; Auger et al. 2010; Treu et al. 2010; Leier et al. 2011; Oguri et al. 2014) which produced stellar mass fractions in the range 30%–70% integrated inside the Einstein radius of the lenses.

2021AandA...656A..16C__Bruno_&_Carbone_2013_Instance_4

Investigations of the turbulent nature of solar wind fluctuations have been ongoing for more than half a century (see, e.g., Bruno & Carbone 2016). Advances have been made consistently thanks to the increasingly accurate measurements of several dedicated space mission as well as to the enormous improvement of numerical calculation, new detailed models and theoretical frameworks, and the development of specific data analysis techniques. Nevertheless, the extremely complex nature of the system and the coexistence of multiple actors, scales, and dynamical regimes have led to a number of questions that remain open (Viall & Borovsky 2020). Among these, the very nature of the turbulent cascade of the solar wind flow and its relationship with the small-scale processes still need to be described in full (Tu & Marsch 1995; Bruno & Carbone 2013; Matthaeus & Velli 2011; Chen 2016). Magnetic field fluctuations have been characterized with great detail at magnetohydrodynamic and kinetic scales, for example, through spectral and high-order moments analysis (Tu & Marsch 1995; Bruno & Carbone 2013). The anisotropic nature of magnetic turbulence has also been addressed, and is still being debated, due to the limited access to three-dimensional measurements in space (see, e.g., Horbury et al. 2008, 2012; Sorriso-Valvo et al. 2010; Yordanova et al. 2015; Verdini et al. 2018; Telloni et al. 2019a; Oughton & Matthaeus 2020). Velocity fluctuations have been studied thoroughly (see, e.g., Sorriso-Valvo et al. 1999; Bruno & Carbone 2013), although the kinetic scales still remain quite unexplored for instrumental limitations, most notably in the sampling time resolution. Both the velocity and magnetic field show highly variable turbulence properties, with well developed spectra, strong intermittency (Sorriso-Valvo et al. 1999), anisotropy, and linear third-order moments scaling (Sorriso-Valvo et al. 2007; Carbone et al. 2011). The level of Alfvénic fluctuations (mostly but not exclusively found in fast streams, see e.g., D’Amicis et al. 2011; Bruno et al. 2019) are believed to be associated with the state of the turbulence. In particular, solar wind samples containing more Alfvénic fluctuations are typically associated with less developed turbulence, as inferred from both shallower spectra and reduced intermittency (see Bruno & Carbone 2013, and references therein). This is consistent with the expectation that uncorrelated Alfvénic fluctuations contribute to reduce the nonlinear cascade by sweeping away the interacting structures (Dobrowolny et al. 1980), as also confirmed by the observed anticorrelation between the turbulent energy cascade rate and the cross-helicity (Smith et al. 2009; Marino et al. 2011a,b).

2021MNRAS.508.2019B__Körding,_Jester_&_Fender_2008_Instance_1

In the context of AGN models, for SAD-dominated sources the ratio Lradio/$L_{\rm X} \propto \eta _{\rm jet} \, \bar{a}^{2}$ (Yi & Boughn 1999) is expected to be constant and limited to a narrow range of values, as we find for our Seyferts, if the $\bar{a}$ range is finite. Conversely, for LINERs, that are powered by RIAF discs, the relation between bolometric AGN and jet power is difficult to model because jet emission can dominate their entire SED (Körding, Jester & Fender 2008) and [O iii]-line contamination from jet photoionizing shocks can overpredict the bolometric AGN power (Capetti, Verdoes Kleijn & Chiaberge 2005; Netzer 2009). However, in a simplistic scenario of a disc origin of the radio emission, ADAF discs would predict much shallower relations ($L_{\rm core} \propto L_{\rm X}^{\alpha }$ with α 0.6 depending on bremsstrahlung-dominated and multiple Compton scattering regimes, Yi & Boughn 1998) than the observed slopes for LINERs (1.2–1.3). Furthermore, the observed radio-[O iii] relation found for LINERs is even steeper (despite the large scatter) than any previous relations between the kinetic jet power and the bolometric AGN luminosities found for LINER-like RL AGN (slopes 1, e.g. Capetti & Balmaverde 2006; Merloni & Heinz 2007; Baldi et al. 2019b). Therefore, assuming that RL LINERs are the scaled-down version of RGs and normalizing the Lradio/LX comparison with previous studies based on this assumption, the steeper slopes might be the consequence of a closer view of jet-launching mechanism even in RQ LINERs. The high sensitivity and sub-arcsec resolution (crucially intermediate between VLA and VLBI) provided by our survey allowed us to probe the parsec-scale region near the core where the jet is launched and reveal the relevant role of Γjet and BH spin/mass in the jet production. These parameters could eventually induce much steeper relations than those established with shallower radio observations.

2020MNRAS.498.1480S__Bharadwaj_&_Sethi_2001_Instance_1

After the recombination epoch, the CMB hardly interacts with the neutral intervening medium. This restricts the CMB from probing the evolution of the structures till the end of EoR. The 21-cm radiation, which is involved in the hyperfine transition of H i, is a promising probe to study the high-redshift universe including EoR (e.g. Sunyaev & Zeldovich 1972; Hogan & Rees 1979). There are existing and the upcoming radio interferometers aiming to observe the brightness temperature fluctuations of the redshifted 21-cm signal from EoR that we coin as the ‘EoR 21-cm signal’. However, the detection of the signal is not yet possible due to the foreground contamination from galactic and extra-galactic source. The foregrounds are ∼104−105 times stronger (e.g. Ali, Bharadwaj & Chengalur 2008; Bernardi et al. 2009, 2010; Ghosh et al. 2012; Paciga et al. 2013; Beardsley et al. 2016) compared to the signal. The foregrounds, system noise, and calibration errors together keep the current observations at bay from directly detecting the EoR 21-cm signal. As a consequence, the first detection is likely to be statistical in nature. These observations plan to measure the power spectrum (PS) of the EoR 21-cm signal (e.g. Bharadwaj & Sethi 2001; Bharadwaj & Ali 2004, 2005). Several radio interferometers such as the GMRT1 (Swarup et al. 1991), LOFAR2 (van Haarlem et al. 2013), MWA3 (Tingay et al. 2013), and PAPER4 (Parsons et al. 2010) have carried out observations to measure the EoR 21-cm PS. However, only few weak upper limits on the PS amplitudes have been reported in the literature to date (e.g. GMRT: Paciga et al. 2011, Paciga et al. 2013; LOFAR: Yatawatta et al. 2013; Patil et al. 2017; Gehlot et al. 2019; Mertens et al. 2020; MWA: Dillon et al. 2014; Jacobs et al. 2016; Barry et al. 2019; Li et al. 2019; Trott et al. 2020; PAPER: Cheng et al. 2018; Kolopanis et al. 2019). A few more upcoming telescopes with improved sensitivity such as HERA5 (DeBoer et al. 2017) and SKA6 (Koopmans et al. 2014) also aim to measure the EoR 21-cm PS. Apart from PS, several other estimators such as the variance (Patil et al. 2014), bispectrum (Bharadwaj & Pandey 2005; Yoshiura et al. 2015; Shimabukuro et al. 2017; Majumdar et al. 2018), and Minkowski functional (Bag et al. 2018, 2019; Kapahtia et al. 2018; Kapahtia, Chingangbam & Appleby 2019) are being used to quantify the EoR 21-cm signal. These estimators are supposed to be rich in information about the underlying physical processes during EoR.

2015AandA...584A..75V__Essen_et_al._(2014)_Instance_3

The data presented here comprise quasi-simultaneous observations during secondary eclipse of WASP-33 b around the V and Y bands. The predicted planet-star flux ratio in the V-band is 0.2 ppt, four times lower than the accuracy of our measurements. Therefore, we can neglect the planet imprint and use this band to measure the stellar pulsations, and most specifically to tune their current phases (see phase shifts in von Essen et al. 2014). Particularly, our model for the light contribution of the stellar pulsations consists of eight sinusoidal pulsation frequencies with corresponding amplitudes and phases. Hence, to reduce the number of 24 free parameters and the values they can take, we use prior knowledge about the pulsation spectrum of the star that was acquired during von Essen et al. (2014). As the frequency resolution is 1/ΔT (Kurtz 1983), 3.5 h of data are not sufficient to determine the pulsations frequencies. Therefore, during our fitting procedure we use the frequencies determined in von Essen et al. (2014) as starting values plus their derived errors as Gaussian priors. As pointed out in von Essen et al. (2014), we found clear evidences of pulsation phase variability with a maximum change of 2 × 10-3 c/d. In other words, as an example after one year time a phase-constant model would appear to have the correct shape with respect to the pulsation pattern of the star, but shifted several minutes in time. To account for this, the eight phases were considered as fitting parameters. The von Essen et al. (2014) photometric follow-up started in August, 2010, and ended in October, 2012, coinciding with these LBT data. We then used the phases determined in von Essen et al. (2014) during our last observing season as starting values, and we restricted them to the limiting cases derived in Sect. 3.5 of von Essen et al. (2014), rather than allowing them to take arbitrary values. The pulsation amplitudes in δ Scuti stars are expected to be wavelength-dependent (see e.g. Daszyńska-Daszkiewicz 2008). Our follow-up campaign and these data were acquired in the blue wavelength range. Therefore the amplitudes estimated in von Essen et al. (2014), listed in Table 1, are used in this work as fixed values. This approach would be incorrect if the pulsation amplitudes would be significantly variable (see e.g., Breger et al. 2005). Nonetheless, the short time span of LBT data, and the achieved photometric precision compared to the intrinsically low values of WASP-33’s amplitudes, make the detection of any amplitude variability impossible.

2019MNRAS.490.5353M__Chen_&_Podsiadlowski_2017_Instance_1

An independent approach to constrain the strength of magnetic fields in accretion flow models in binary systems is by measuring how much angular momentum is lost from the system via magnetized outflows and estimating the corresponding orbital decay in the system due to magnetic braking. The orbital decay in A0620-00 is rapid, with orbital-period derivative $\dot{P} =-0.6 \, {\rm [ms \, yr^{-1}]}$ (González Hernández, Rebolo & Casares 2014), and it cannot be explained by the emission of gravitational waves alone. Magnetic braking of the system is a possible explanation for the measured $\dot{P}$, but other explanations, such as resonant interactions between the binary and the possible circumbinary disc, have been proposed (Chen & Podsiadlowski 2017). Here we can estimate the magnitude of the magnetic braking of the system using a first-principles approach. The orbital angular momentum of a binary system is $J_{\rm orb}= M_{\rm BH}M_*/(M_{\rm BH}+M_*) \sqrt{G(M_{\rm BH}+M_*) d}$, where d is the separation between the star and the black hole. In GRMHD simulations, the loss of total angular momentum through the outer boundary is defined as $\dot{J}(r_{\rm out},t)=\int _\theta \int _\phi T^r_\phi \,\mathrm{ d}A_{\theta \phi }$, where $T^r_\phi \equiv (\rho + \gamma u + b^2) u^r u_\phi -b^r b_\phi$ is the stress–energy tensor describing the radial flux of angular momentum. The quantity u is the internal energy of the gas, γ = 4/3 is the adiabatic index, uμ is the four-velocity of the gas and bμ is a four-vector that describes the magnetic field in a frame comoving with the gas. We integrate the above formula at rout = 50GM/c2 over θ ∈ (0, π) and ϕ ∈ (0, 2π). In our best-fitting model, the ratio of angular momentum flux through the outer boundary to the orbital angular momentum is extremely low: $\dot{J}/J_{\rm orb} \approx 10^{-22}$ [s−1], which could account for an orbital period derivative of $\dot{P} = 2 \times 10^{-8} {\rm [ms \, yr^{-1}]}$ only. Our calculations confirm that the inner highly magnetized, rotating accretion flow (with current $\dot{M}$) alone cannot be responsible for the rapid orbital decay observed in the system and this favours the idea of a circumbinary disc.

2020MNRAS.492.1295P__Bonning_et_al._2012_Instance_2

The evolution of colour or the spectral index, α, (F(ν) ∝ ν−α where ν is the radiation frequency and F(ν) is the flux density provides an insight into the particle distribution giving rise to the observed flux density and its variability. In particular, at the synchrotron frequencies, within the simplest scenario of single-zone emission models with homogeneous magnetic field distributions, clear patterns between the spectral index and the total intensity are predicted, i.e. a ‘spectral hysteresis’, depending on the relative lengths of the radiative cooling time-scale and the escape time-scale of the accelerated particles from the emission zone (e.g. Kirk, Rieger & Mastichiadis 1998). A significant fraction of long-term multiband flux monitoring studies have revealed bluer-when-brighter trends for BL Lac objects but frequently redder-when-brighter trends for FSRQs (Gu et al. 2006; Osterman Meyer et al. 2009; Hao et al. 2010; Rani et al. 2010; Ikejiri et al. 2011; Bonning et al. 2012; Sandrinelli, Covino & Treves 2014; Li et al. 2018; Meng et al. 2018; Gupta et al. 2019) while ‘achromatic’ flux variability (no colour evolution, Stalin et al. 2006; Bonning et al. 2012; Gaur et al. 2019), and erratic patterns (Wierzcholska et al. 2015) have also been reported. It has been argued that particles accelerated to higher energies are injected at the emission zone before being cooled radiatively in BL Lac sources leading to their overall SEDs being bluer-when-brighter; however, the ‘redder’ and more variable jet-component can overwhelm the ‘bluer’ contribution from the accretion disc, leading to redder-when-brighter trends for FSRQ type sources (Gu et al. 2006). Achromatic variability is often ascribed to changes in the Doppler boosting factor (δ) as each frequency notes the same special relativistic multiplication of flux (Gaur et al. 2012). However, erratic colour trends together with the opposite behaviours, i.e. redder-when-brighter changes for BL Lacs (Gu & Ai 2011) and bluer-when-brighter trends for FSRQs (Wu et al. 2011), indicate that more complex scenarios, presumably involving the dominance of the relative contributions of the Doppler boosted jet emission component and the accretion disc component, respectively, are particularly relevant for blazars with peak synchrotron frequencies in the range of 1013–15 Hz (low-frequency peaked blazars; Isler et al. 2017; Gopal-Krishna, Britzen & Wiita 2019).

2019AandA...630A.131M__Uttley_et_al._2014_Instance_1

Comptonisation Monte Carlo code (MoCA; see Tamborra et al. 2018 for a detailed description of the code) is based on a single photon approach, working in a fully special relativistic scenario. MoCA allows for various and different physical and geometrical conditions of the accretion disc and of the Comptonising corona. In this paper, the corona is assumed to have either a spherical or a slab-like geometry, and to be as extended as the disc, whose radii have been set to be Rout = 500 rg and Rin = 6 rg, respectively. Even though arguments (e.g. variability, Uttley et al. 2014, and references therein, microlensing Chartas et al. 2009; Morgan et al. 2012 and timing Kara et al. 2016; De Marco et al. 2013) exist that favour a compact corona, we used extended coronae. In fact, as discussed by Marinucci et al. (2019), Comptonised spectra emerging from compact corona (Rout = 100 rg–Rin = 6) do not deviate significantly from those produced in more extended corona; see their Fig. 3. The adoption of even more compact coronae (Rout = 20 rg, Rin = 6) results only in the need for higher optical depths to recover the same spectral shape for a given temperature. However, in such cases, general relativity (GR) effects are not negligible (see Tamborra et al. 2018, for a detailed discussion on this topic), and the present version of MoCA does not include GR. For the slab-like geometry case, MoCA allows the user to set up the corona height above the accretion disc (set to 10 rg in our simulations). We use synthetic spectra computed assuming the source BH mass and accretion rate to be the same as those of Ark 120 (e.g. Marinucci et al. 2019, and references therein), namely MBH = 1.5 × 108 M⊙ and ṁ = Lbol/LEdd = 0.1. For both the slab and spherical hot electron configurations, we simulated the Comptonised spectra using a wide range of values for electron temperature and optical depth: 0.1   τ   7 and 20 kT 200 keV, and in Fig. 1 we show a sample of spectra obtained by MoCA. Moreover, spectra are computed from 0.01 keV up to 700 keV using 1000 logarithmic energy bins, and a Poissonian error accompanies each spectral point. The obtained spectra are averaged over the inclination angle and in Fig. 1 we show some exemplificative spectra normalised at 1 keV accounting for the two geometries considered in this work.

2021AandA...656A..44R__Sérsic_1968_Instance_1

The detection of LSBGs is usually carried out by a sequence of tasks. The first step is a broad detection of LSBG candidates. Both the depth of the data and the efficiency of this detection in the low-surface-brightness regime will define the completeness of the sample, and so a high efficiency in the detection of diffuse sources in this first step is a key point in the process. Given the importance of this primary detection, it is common to use specialized software or procedures (e.g., Akhlaghi & Ichikawa 2015; Prole et al. 2018; Haigh et al. 2021). After a first detection of potential objects, a characterization of their structural and morphological properties is necessary in order to accurately define the sample, typically with certain criteria in surface brightness and radius. To obtain the structural parameters of the LSBG candidates, the detected sources are usually fitted with a Sérsic model (Sérsic 1968). Specific procedures such as GALFIT (Peng et al. 2010) or IMFIT (Erwin 2015) are used to obtain accurate morphological and structural parameters. However, the computational cost of these is very high, becoming a bottleneck in LSBG analysis pipelines. For this reason, it is advisable to minimize the number of LSBG candidates prior to their Sérsic fitting to save computational time. This is not trivial because structural parameters from automated segmentation catalogs are much less accurate than Sérsic fit modeling, making correct screening of the sample challenging. As a last step, a “supervision” of all the objects matching the criteria of the sample is necessary in order to discard the frequent presence of false positives. These are sources that, although meeting the imposed criteria, are clearly not LSBGs. Examples of false positives are clumped sources that have been considered as a single source, faint reflections due to the optics of the instrumentation, or artifacts present in the images. Depending on the data volume analyzed, the number of objects to be supervised can become very high. For this reason, it is increasingly common to use deep learning techniques to screen objects in order to eliminate these false-positive detections (e.g., Tuccillo et al. 2018; Burke et al. 2019; Tanoglidis et al. 2021a). However, unsupervised or automated detections can not yet reach the accuracy that human visual inspection is capable of obtaining, reaching almost 100%. Therefore, human supervision is desirable, when possible, in order to minimize the presence of false positives in samples of reasonably limited volume, such as that used here.

2021ApJ...919...30D__Staguhn_et_al._2014_Instance_1

The first SMGs were detected using SCUBA at 850 μm (Smail et al. 1997; Barger et al. 1998; Hughes et al. 1998), which remains one of the prime wavelengths to detect these galaxies (e.g., Geach et al. 2017), thanks to a combination of available instruments, spectral window, and the negative k-correction at that wavelength. Other single-dish samples of SMGs have also been obtained at 1.1–1.3 mm using MAMBO (e.g., Eales et al. 2003; Bertoldi et al. 2007; Greve et al. 2008) and AzTEC (e.g., Aretxaga et al. 2011; Yun et al. 2012), at 1.4 mm/2 mm with the SPT (Vieira et al. 2010), and at 2 mm with GISMO (Staguhn et al. 2014; Magnelli et al. 2019). Selecting SMGs from observations at longer wavelengths is thought to favor galaxies at higher redshifts (e.g., Smolčić et al. 2012; Vieira et al. 2013; Staguhn et al. 2014; Magnelli et al. 2019; Hodge & da Cunha 2020), although it is difficult to compare the redshift distributions in an unbiased way (see, e.g., Zavala et al. 2014 for a discussion), and account for intrinsic variations of galaxy far-IR spectral energy distributions (SEDs). Nevertheless, the 2 mm band has been put forth as a potential candidate to detect high-redshift (z > 3) galaxies (e.g., Casey et al. 2018a, 2018b, 2019; Zavala et al. 2021). The negative k-correction is stronger at 2 mm than at 850 μm; thus, for a fixed SED, the 2 mm band should pick up more high-redshift galaxies than at 870 μm. In addition, better atmospheric transmission and larger fields of view can be achieved at 2 mm (but corresponding poorer resolution). Such an effort is currently ongoing (see Zavala et al. 2021 for first results). To understand the relationship between the populations detected at 850 μm and at 2 mm, we require a detailed characterization of the (sub)millimeter SEDs of these sources. Multiwavelength submillimeter observations are still rare, with most observations focusing on a single wavelength. Only a handful of sources observed at 2 mm have complementary shorter-wavelength detections (Staguhn et al. 2014; Magnelli et al. 2019). Thus, a more systematic multiwavelength dust continuum investigation is warranted in order to reveal the dust properties of (sub)millimeter-detected sources.

2018AandA...620A..84B__Hua_et_al._1998_Instance_1

The PN IC 5148 (PN G002.7-52.4) is a nebula which is to date not well investigated in detail. First listed in the Second Index Catalogue of Nebulae and Clusters of Stars (Dreyer 1910) with two independent entries as numbers IC 5148 and IC 5150 discovered by Swift (1899) and Gale (1897) independently, it was finally discovered to be the same object by Hoffmeister (1961). Morphologically it is declared as a round nebula in all catalogs and Chu et al. (1987) classified it as a multiple shell planetary nebula (MSPN) due to a small step in the H α image with a radius ratio of only 1:1.2 between the two structures. In addition, the intensity decrease was found smaller than that for typical MSPNe. A few years later the authors searched with a larger field systematically around many nebulae for extended emission features without detecting the very low surface brightness halo we investigate here (Hua et al. 1998). While earlier spectroscopic studies took only a small fraction of one or two spectral lines to obtain radial velocity and expansion of the nebula (e.g. Meatheringham et al. 1988), to our knowledge up to now the only, more extended spectroscopic analysis were performed by Kaler et al. (1990) and by Kingsburgh & Barlow (1994) within two surveys of 75 and 80 southern PNe, respectively. Both studies used pre-CCD electronic spectral scanning devices, taking a very small aperture region of the nebula and detected only a hand full of lines. They end up coinciding in the result that the nebula has about galactic disk abundance or only slight underabundance despite its large galactic latitude (b approximately −52°). Further, thesurvey spectra used in the PN catalog of Acker et al. (1992) gives intensities of only four lines. The inspection of the original data file of this survey, provided now at the Hong Kong/AAO/Strasbourg PN data base (thereafter HASH2; Parker et al. 2016; Bojičić et al. 2017), does not recover more usable lines above the noise level.

2015ApJ...811L..32H__Matthews_1994_Instance_1

In this Letter, we directly test the relationship between proton kinetic instabilities and plasma turbulence in the solar wind using a hybrid expanding box model that allows us to study self-consistently physical processes at ion scales. In the hybrid expanding box model, a constant solar wind radial velocity vsw is assumed. The radial distance R is then , where R0 is the initial position and is the initial value of the characteristic expansion time Transverse scales (with respect to the radial direction) of a small portion of plasma, comoving with the solar wind velocity, increase ∝ R. The expanding box uses these comoving coordinates, approximating the spherical coordinates by the Cartesian ones (Liewer et al. 2001; Hellinger & Trávníček 2005). The model uses the hybrid approximation where electrons are considered as a massless, charge-neutralizing fluid and ions are described by a particle-in-cell model (Matthews 1994). Here, we use the two-dimensional (2D) version of the code, fields and moments are defined on a 2D x–y grid 2048 × 2048, and periodic boundary conditions are assumed. The spatial resolution is Δx = Δy = 0.25dp0, where is the initial proton inertial length (vA0: the initial Alfvén velocity, Ωp0: the initial proton gyrofrequency). There are 1024 macroparticles per cell for protons that are advanced with a time step , while the magnetic field is advanced with a smaller time step The initial ambient magnetic field is directed along the radial z-direction, perpendicular to the simulation plane , and we impose a continuous expansion in the x- and y-directions. Due to the expansion, the ambient density and the magnitude of the ambient magnetic field decrease as (the proton inertial length dp increases ∝ R; the ratio between the transverse sizes and dp remains constant; the proton gyrofrequency Ωp decreases as ∝R−2). A small resistivity η is used to avoid accumulation of cascading energy at grid scales; initially, we set (μ0 being the magnetic permittivity of vacuum) and η is assumed to be The simulation is initialized with an isotropic 2D spectrum of modes with random phases, linear Alfvén polarization ( ), and vanishing correlation between magnetic and velocity fluctuation. These modes are in the range 0.02 ≤ kdp ≤ 0.2 and have a flat one-dimensional (1D) power spectrum with rms fluctuations = 0.24 B0. For noninteracting zero-frequency Alfvén waves, the linear approximation predicts (Dong et al. 2014). Protons initially have the parallel proton beta and the parallel temperature anisotropy as typical proton parameters in the solar wind in the vicinity of 1 AU (Hellinger et al. 2006; Marsch et al. 2006). Electrons are assumed to be isotropic and isothermal with βe = 0.5 at t = 0.

2021MNRAS.502.4858S__Yung_et_al._2020_Instance_1

One of our main long-term goals is to work towards a full forward modelling pipeline for multiwavelength galaxy surveys. Over the next decade, wide area surveys from DESI, VRO, Euclid, the Nancy Grace Roman Space Telescope, 4MOST, and other facilities will be carried out. We can use the legacy observations from surveys such as CANDELS, to build a foundation for interpreting these new surveys. What we have shown here is that the current generation of SAMs produce decent broad agreement with key properties of galaxy evolution as represented by CANDELS over the redshift range 0.5 ≲ z ≲ 3. It has been shown elsewhere that these models produce similar results to those of numerical cosmological simulations and other SAMs (Somerville & Davé 2015), and that they are also in agreement with higher redshift observations of galaxy populations (Yung et al. 2019a, b), the reionization history (Yung et al. 2020), and observational probes of the cold gas phase in galaxies (Popping, Somerville & Trager 2014; Popping et al. 2019). While there are certainly remaining tensions with observations, as seen here and also in, e.g. Popping et al. (2019), there is promising ongoing work to continue to improve the realism of the treatment of physical processes in SAMs (e.g. Pandya et al. 2020). In work in progress, we are using this framework to create similar mock observations for future planned surveys with the James Webb Space Telescope and the Nancy Grace Roman Space Telescope (L.Y.A., Yung et al., in preparation). SAMs coupled with light-cones extracted from large volume N-body simulations have recently been used to create a 2 deg2 light-cone from 0 z 10 (Yang et al. 2020; Yung et al., in preparation). In order to create mock surveys for even larger areas – tens to hundreds of square degrees – that will be probed by the projects mentioned above, it is likely that new, even more computationally efficient techniques will need to be developed, perhaps enabled by machine-learning-based tools.

2019ApJ...882...65M__Kennicutt_et_al._2003_Instance_1

Given this correlation between dust luminosity and SFR, it is reasonable to expect the dust mass to scale with SFR as well. In the top panel of Figure 10 we show this relation for our data with the additional parameter of the dust temperature indicated by color. This dust temperature is a weighted average of the temperatures of the two components of the dust model, namely the ISM and birth clouds. Using MAGPHYS, da Cunha et al. (2010) found a tight correlation between dust mass and SFR in a local sample derived from the Sloan Digital Sky Survey, which we show as a black line. The da Cunha et al. (2010) sample is selected to be star-forming by emission line diagnostics and lies at z ≤ 0.2. It is also important to note that the SFRs for their sample lie mostly below ∼20 M⊙ yr−1. The solid portion of the line represents the extent of their data. We also show the Spitzer Infrared Nearby Galaxy Sample (Kennicutt et al. 2003) analyzed in da Cunha et al. (2008) as purple stars for comparison. At first, our data do not appear to follow any trend, with only our highest measured dust masses falling near the da Cunha et al. (2010) relation. Given that MAGPHYS allows a range of dust temperatures when fitting for the dust mass, we investigate the effects of dust temperature in this diagram. When we examine the temperature of the dust (which MAGPHYS allows to vary from 20 to 80 K) as indicated by color in the figure, we see that sources with lower dust temperature actually lie on the published relation. If we consider bands of constant dust temperature in the diagram we see that the slope of the Mdust–SFR relation for our sources actually closely matches the slope of the da Cunha et al. (2010) relation. As shown in Figures 6 and 7, we observe a slight increase in average dust temperature with redshift for our sample, so it may be tempting to interpret the variation in this diagram as a redshift dependence. We checked the relation between dust temperature and redshift for our sample and found the scatter in temperature at a given redshift to be much larger than the evolution between redshift bins. This suggests that the local calibration between dust mass and SFR can be extended to higher redshift samples through the incorporation of the additional parameter of dust temperature. Given our particular sample selection, the application of these results to a more general star-forming population would have to be done with caution.

2015ApJ...806..199B__Balser_et_al._2011_Instance_1

The most prominent metallicity structure in the Milky Way disk is the decrease of metallicity with increasing Galactic radius—the radial metallicity gradient. All of the major tracers reveal radial gradients, typically with slopes between −0.03 and −0.09 . The radial metallicity gradient can be explained by an inside-out galaxy formation where the disk grows via a radially dependent gas infall rate and star formation rate (e.g., Matteucci & Françoi 1989). But why is there such a wide range of radial gradient slopes measured? There are several possibilities. (1) Measurement uncertainty. Measurement errors will cause some variations, but it is unlikely to produce a factor of 3 difference in slope. Homogeneity in observing procedures and data analysis may be more important given the variations in abundance that can be derived for the same source (for further discussion see Rudolph et al. 2006; Henry et al. 2010; Balser et al. 2011). (2) Dynamical evolution. Radial gradients calculated with stellar tracers may be affected by radial migration, where stars are scattered into different orbits (Sellwood & Binney 2002). Radial migration should flatten the radial metallicity gradient, but there is some evidence that this may not be a large factor for stars in the Milky Way (Di Matteo et al. 2013; Kubryk et al. 2013; Bovy et al. 2014). (3) Temporal evolution. Many tracers have a wide range of age and therefore are probing the Milky Way disk at different times. For example, the radial gradient has been observed to flatten with time when using Open clusters (e.g., Friel et al. 2002). But accurate ages are crucial to separate out these temporal effects. PN studies indicate a flattening of the radial gradient with time (Maciel et al. 2003), a steepening with time (Stanghellini & Haywood 2010), or no temporal variation (Henry et al. 2010). These vastly different conclusions reveal the difficulty in deriving accurate PN ages and distances. (4) Sample Evolution. Hayden et al. (2013) measure a flattening of the radial gradient away from the Galactic mid-plane. Therefore, including sources out of the Galactic plane, which may be from an older population, may alter the derived radial gradient slope (see Minchev et al. 2014). Azimuthal abundance variations have been reported (e.g., Pedicelli et al. 2009; Balser et al. 2011, Section 3.3). If real, these will complicate any analysis of radial gradients. Here we detect metallicity gradient slopes over different azimuth ranges that span the values observed in the literature. The usual assumption is that the disk is well mixed at a given radius but this may not be true.

2019ApJ...871..243Y__Saito_et_al._1999_Instance_1

There are two possibilities resulting in the different magnetic field strengths inferred from the polarimetric and molecular-line observations: (1) the rotational-to-gravitational energy βrot is overestimated, and (2) there are additional contributions in the polarized intensity from other mechanisms, such as dust scattering. In our MHD simulations, βrot is adopted to be 0.4% based on the observational estimates of the core mass of ∼1 M and the angular speed of the core rotation of 4 × 10−14 s−1. The angular speed was estimated based on the global velocity gradient along the major axis of the dense core observed with single-dish telescopes (Saito et al. 1999; Yen et al. 2011; Kurono et al. 2013). Numerical simulations of dense cores including synthetic observations show that the specific angular momentum derived from the synthetic images of the dense cores can be a factor of 8–10 higher than their actual specific angular momentum computed by the sum of the angular momenta contributed by the individual gas parcels in the dense cores (Dib et al. 2010). In addition, if there are filamentary structures in the dense core in B335, which could not be resolved with the single-dish observations, infalling motions along the filamentary structures could also contribute to the observed velocity gradient, leading to an overestimated angular speed of the core rotation (Tobin et al. 2012). We have also performed our simulations with a lower βrot, and we find that the rotational velocity on a 100 au scale in the simulations decreases with decreasing βrot. Thus, the discrepancy in the magnetic field strengths inferred from the field structures and the gas kinematics can be reconciled, if the core rotation in B335 is overestimated by a factor of a few in the observations, and these results would suggest a weak magnetic field of initial λ of 9.6 in B335. Further observations combining single dishes and interferometers to have a high spatial dynamical range and to map the velocity structures of the entire dense core in B335 at a high angular resolution are needed to study coherent velocity features and provide a better estimate of the core rotation.

2015AandA...575A.111D__Pinsonneault_et_al._(2001)_Instance_1

The difference in iron abundance between the two XO-2 stellar components poses an interesting question. They belong to a visual binary and, as normally assumed for such systems, they should share the same origin and initial bulk metallicity. A relevant characteristic of this system is that both of the stars host planets. Thus, for components of wide binaries where at least one star has a planet, a reasonable hypothesis to explain any measured and significant difference in their present-day elemental abundances is that the planet formation process had played a relevant role. The higher iron abundance of XO-2N when compared to XO-2S might be due to past ingestion of dust-rich or rocky material that came from the inner part of the proto-planetary disk and that was pushed into the host star by the hot Jupiter XO-2N as it migrated inward to its current orbit. A second mechanism, which acts on a different time scale and after the pre-main-sequence stage, is presented by Fabrycky & Tremaine (2007), who discuss the pollution of the stellar photosphere with metals produced by mass loss of inward migrating hot Jupiters. In addition, Kaib et al. (2013) showed with their simulations that very distant binary companions may severely affect planetary evolution and influence the orbits of any planet around the other component of the system, perhaps favouring the ingestion of material by the host star. Following the results shown in Fig. 2 of Pinsonneault et al. (2001), we roughly estimated that XO-2N could have ingested an amount of iron slightly higher than 5 M⊕ to increase its photospheric iron content by ~0.05 dex, given its Teff ~ 5300 K. The difference in the iron abundance of the two XO-2 stellar components is similar to that found by Ramírez et al. (2011) for the solar twins 16 Cyg A and 16 Cyg B (Δ [Fe/H] = 0.042 ± 0.016 dex). These stars have masses close to those of the XO-2 companions and, together with a third companion 16 Cyg C, are members of a hierarchical triple system, where the A and C components form a close binary with a projected separation ~70 AU. The component B is known to host a giant planet in a long-period and highly eccentric orbit (P ~ 800 days and e ~ 0.69)5, but unlike the case of XO-2 it is the star with a lower iron content. To explain this deficiency, Ramírez et al. (2011) suggest that an early depletion of metals happened during the formation phase of the 16 Cyg Bb planet. An alternative and suggestive possibility is that 16 Cyg A may have ingested a massive planet that enriched the star with iron, while the large orbit of 16 Cyg Bb (with semi-major axis of 1.68 AU) very likely prevented any mass loss from the planet. We note that, although Δ[Fe/H] is similar to that found for XO-2, the amount of iron involved should be different. The XO-2 stars are cooler than 16 Cygni A and B and therefore have more massive convective envelopes, implying that more iron should be [is?] necessary to pollute the XO-2N photosphere and produce almost the same Δ[Fe/H]. Laws & Gonzalez (2001) also found the primary of the 16-Cyg system enhanced in Fe relative to the secondary. Similar studies conducted in binary stars hosting planets (see e.g. Gratton et al. 2001; Desidera et al. 2004, 2006; Schuler et al. 2011a; Teske et al. 2013; Liu et al. 2014; Mack et al. 2014) did not find relevant differences in elemental abundance among the components. All these results imply that the presence of giant planets does not necessarily imply differences in the chemical composition of the host star.

2017AandA...607A..85L__Zeipel_1924_Instance_1

The LCs analyses were made using the phoebe v.0.29d software (Prša & Zwitter 2005) that is based on the 2003 version of the Wilson-Devinney (W-D) code (Wilson & Devinney 1971; Wilson 1979, 1990). In the absence of spectroscopic mass ratios, the “q-search” method (for details see Liakos & Niarchos 2012) was applied in modes 2 (detached system), 4 (semi-detached system with the primary component filling its Roche lobe) and 5 (conventional semi-detached binary) to find feasible (“photometric”) estimates of the mass ratio. The step of q change during the search was 0.05–0.1 starting from q = 0.05−0.1. The effective temperatures of the primaries (T1) were given the values derived from the spectral classification (see Sect. 2) and were kept fixed during the analysis, while the temperatures of the secondaries T2 were adjusted. The Albedos, A1 and A2, and gravity darkening coefficients, g1 and g2, were set to generally adopted values for the given spectral types of the components (Rucinski 1969; von Zeipel 1924; Lucy 1967). The (linear) limb darkening coefficients, x1 and x2, were taken from the tables of van Hamme (1993). The dimensionless potentials Ω1 and Ω2, the fractional luminosity of the primary component L1, and the orbital inclination of the system i were set as adjustable parameters. At this point it should to be noted that since the Kepler’s photometer has a spectral response range between approximately 410–910 nm with a peak at ~ 588 nm, the R filter (Bessell photometric system-range between 550–870 nm and with a transmittance peak at 597 nm) was selected as the best representative for the filter depended parameters (i.e. x and L). Moreover, there is evidence of maxima brightness changes in all of the systems, therefore parameters of photospheric spots on the surface of the secondary were also adjusted. The selection of the magnetically active component was based on the effective temperatures of the members of the systems. In all cases the secondaries are clearly cooler than the primaries (i.e. large minima difference), therefore, they host a convective envelope that better suits a magnetically active star. In addition, the hotter stars are candidates for δ Sct type pulsations and it is rather rare to present magnetic activity also. For all EBs, except for KIC 111, the third light parameter (l3) was also adjusted because the systems are candidates for triplicity (see Sect. 1). However, during the iterations it resulted in unrealistic values, therefore, it was omitted in the final analysis. Finally, all systems were found to have the minimum ∑ res2 in mode 5. KIC 066 and KIC 111 have a minimum at q = 0.3, while KIC 105 and KIC 106 at q = 0.15. In Fig. 7 the respective q-search plots are shown.

2015ApJ...800...72T__Kroupa_1995_Instance_1

The binarity or binary fraction, f, is defined as the ratio of the number of binary or higher-order systems, Nbin, to the total number of systems, Nsys. Here, the term system includes multiple systems and singles (their number being noted as Nsng) as well. Then 6For the star-like population, we apply the binary DPS method developed by Marks et al. (2011) and Marks & Kroupa (2011), hereafter referred to as dynamical or DPS pairing. In DPS the binary stars are formed in a population of embedded clusters, within which they are dynamically processed to yield the Galactic disk stellar single-plus-binary population. An attractive feature of this theory is its underlying assumption of the universality of binary properties of late-type stars being consistent with observational data (Marks & Kroupa 2012; Leigh et al. 2014). For initial binaries with intermediate to large separations, the DPS pairing method applies random pairing11M. Marks et al. (2014, in preparation) show that random pairing with subsequent dynamical processing does indeed reproduce at least the observed low-mass stellar population (see also Kroupa 1995). below a primary mass of 5 M and ordered pairing (such that q ⩾ 0.9) above. Here, q = mcomp/mprim ⩽ 1, where mcomp is the companion mass and mprim is the mass of the primary star. This initial binary population is then altered by dynamical evolution. Close binaries with orbital periods below about 10 days undergo eigenevolution (Kroupa 1995) and tend to equalize the companion masses. Note that this eigenevolution term alters the very-low-mass end of the star-like IMF. For the purpose of this work, however, these effects only play a negligible role. Here, the initial or primordial binary fraction is 100%; that is, it is assumed that all stars form in binaries. The final (after dynamical processing in the embedded cluster) overall binary fraction is about 40% (i.e., f = 0.4) but varies as a function of the primary-star mass. For M dwarfs, in particular, it is as low as 25%, whereas G dwarfs show about 56% binarity. The binary fraction approaches 90% for O stars. For the BD-like population, we chose an overall binary fraction of 20% (i.e., f = 0.20), in accordance with TK07 and TK08. About half of the members of observed average stellar populations are binaries, most of them remaining unresolved in typical star-cluster surveys. However, very young and likely dynamically unevolved populations like the Taurus–Auriga association exhibit almost 100% binarity (Kroupa et al. 2013; Duchêne & Kraus 2013; Reipurth et al. 2014). The number of systems must not be confused with the number of individual bodies: Nbod = Nsng + 2Nbin. Because higher-order multiples are relatively rare (Goodwin & Kroupa 2005), they are summarized within the binary population in this work, so the total number of bodies is 7The CMRD describes the relative number of binaries as a function of the companion-to-primary mass ratio. Observations reveal a continuous decline of f as a function of the primary-object mass, which has been interpreted as a continuous transition from the stellar to the substellar regime (Joergens 2008; Kraus & Hillenbrand 2012; but see Thies & Kroupa 2008). There is also a shift toward more equal-mass binaries (q = 1) for VLMSs and BDs (Dieterich et al. 2012). These properties of the stellar population are well reproduced by DPS such that the origin and properties of binary populations are well understood.

2016ApJ...822...72C__Mandrini_et_al._2005_Instance_1

Here, for the first time, we have identified highly dynamic non-potential activity on QS-like supergranular network scales. These events overlie mixed polarity network flux elements near the spatial resolution of HMI, and are the first non-potential structures to be unassociated with strong concentrations of bipolar magnetic flux. One event (2011b August 05) shows eruptive activity in the form of jets, which is similar to larger-scale micro-sigmoids (Raouafi et al. 2010) and even QS bright point sigmoids (Chesny et al. 2015). The existence of flaring non-potential fields in the QS-like mixed network immediately shows that supergranular-scale magnetic fields can support processes similar to sigmoid formation (Chesny et al. 2013). Strong non-potential field arcades have been observed in hot X-ray sigmoids on scales of hundreds of arseconds (Moore et al. 2001; Gibson et al. 2002; Liu et al. 2010), micro-sigmoids in soft X-ray imaging on scales of ∼50″ (Mandrini et al. 2005; Raouafi et al. 2010) and small-scale AR EUV (Zheng et al. 2012, 2013), and now at EUV temperatures in QS-like mixed network fields with lengths down to ∼10″. This range of lengths over a range of temperatures and magnetic field topologies points directly to self-similar mechanisms influencing plasma and magnetic field dynamics at a range of scales. Our findings suggest that the processes driving some large-scale eruptions (i.e., flux emergence, helicity build-up, and flux cancellation leading to non-potential field heating (Chen et al. 2014)), can also manifest in a range of configurations on sub-network size scales in QS-like magnetic field configurations. These QS flaring non-potential fields are similar to their large-scale counterparts, but not as strict in their evolution. The diversity in the observed topologies may scale with the diversity of magnetic configurations that exist in the supergranulation network. QS non-potential fields evolve in multi-polar environments, and are not restricted to strongly bipolar dominated regions as in larger-scale, higher temperature events. Despite this, one of the presented events (2011a August 05) results in a post-flare potential loop arcade, which is similar to some observed AR sigmoid fields (Moore et al. 2001).

2019MNRAS.482.3757B__Rankin_1990_Instance_1

Subpulse drifting has been a topic of intensive research with the phenomenon expected to be present in 40–50 per cent of the pulsar population. There are around 120 pulsars known at present to exhibit some form of periodic modulations in their single-pulse sequence (Weltevrede et al. 2006, 2007; Basu et al. 2016). The drifting effects are very diverse and associations are seen with other phenomena like mode changing and nulling in some cases (Wright & Fowler 1981; Deich et al. 1986; Hankins & Wolszczan 1987; Vivekanand & Joshi 1997; Redman et al. 2005; Basu & Mitra 2018b). However, to gain a deeper understanding of the phenomenon at large, more comprehensive studies involving classification of the drifting population and identification of underlying traits are essential. The first such attempt was undertaken by Rankin (1986) who estimated the drifting behaviour within the empirical core–cone model of the emission beam. The pulsar profile, which is formed after averaging several thousand single pulses, has a highly stable structure and is made up of one or more (typically 5) components. The components can be classified into two distinct categories: the central core component and the adjacent conal components. A number of detailed studies suggest that the radio emission beam consists of a central core emission surrounded by conal emission arranged in nested rings (Rankin 1990, 1993; Mitra & Deshpande 1999). The differences in the observed profiles are a result of the different line of sight (LOS) traverses across the emission beam. Rankin (1986) suggested subpulse drifting to be primarily a conal phenomenon, and the drifting to arise due to the circulation of the conal emission around the magnetic axis. This would result in an association between the drifting properties (particularly the phase variation of the drifting feature across the pulse) and the profile classification. The most prominent drifting with clear drift bands and large phase variations was associated with the conal single (Sd) and barely resolved conal double (D) profile classes. This corresponded to the LOS traversing the emission beam towards the outer edge. Progressively more interior LOS traverses of the emission beam result in well-resolved conal double (D), conal Triple (cT), and conal Quadruple (cQ) profile shapes. In the above classification scheme, such profile classes were expected to show primarily longitude stationary drift with very little phase variations. The core-dominated profiles were associated with central LOS traverses of the emission beam. The principal profile classes were categorized as core single (St), Triple (T) with a central core component and pair of conal outriders, and Multiple (M) with central core and two pairs of conal components. Subpulse drifting was expected to be phase stationary and only seen in the conal components of the T and M class profiles. However, the core components sometimes show longer periodic structures that were not classified as subpulse drifting.

2020ApJ...905..111Z__Jirička_et_al._2001_Instance_1

Surveys of radio bursts in decimetric wavelengths is presented in papers by Isliker & Benz (1994) and Jirička et al. (2001), within 1–3 GHz and 0.8–2.0 GHz frequency ranges, respectively. Some of these bursts are still not well understood. This is a case of the slowly positively drifting bursts (SPDBs). They appear in groups or as single bursts, with a duration of an individual burst from 1 to several seconds and their frequency drift is lower than about 100 MHz s−1 (Jirička et al. 2001). The SPDBs seem to be similar to the reverse type III bursts (Aschwanden 2002) but their frequency drift is much smaller. The majority of observed SPDBs are connected to solar flares (Jirička et al. 2001), and they appear many times at the very beginning of the flares (Benz & Simnett 1986; Kotrč et al. 1999; Kaltman et al. 2000; Karlický et al. 2018). Kaltman et al. (2000) reported on several SPDBs observed during three solar flares in the 0.8–2 GHz frequency range. They found frequency drifts of the observed SPDBs to be within the 20–180 MHz s−1 range. Kotrč et al. (1999) studied one of those flares. By combining the radio and spectral plus imaging Hα observations, they explained the observed SPDBs as radio emission generated by downwards propagating shock waves. Based on numerical simulations of the formation of thermal fronts in solar flares, Karlický (2015) proposed that SPDBs observed in the 1–2 GHz range could be a signature of a thermal front. Furthermore, Karlický et al. (2018) reported the observation of an SPDB (1.3–2.0 GHz) observed during the impulsive phase of an eruptive flare. They found time coincidence between the SPDB occurrence, an appearance of an ultraviolet (UV)/EUV multithermal plasma blob moving down along the dark Hα loop at approximately 280 km s−1, and the observed change of Hα profile at the footpoint of that dark loop. Combining these observations they concluded that observed SPDB was likely generated by the thermal front formed in front of the falling EUV blob.

2022MNRAS.515.4430M__Iucci_et_al._1979b_Instance_1

Forbush decreases (FD) are the results of influence of solar wind (SW) large-scale disturbances on the background cosmic ray (CR) flux. They often demonstrate relatively fast CR intensity decrease, accompanied by large and extremely variable CR anisotropy, which is followed by a slower recovery (Forbush 1937; Lockwood 1971; Iucci et al. 1979a; Belov 2009). Most FDs have a sporadic character and are induced by interplanetary disturbances such as Interplanetary Coronal Mass Ejections (ICMEs) caused by coronal mass ejections (CMEs) (Cane 2000; Gopalswamy 2010a; Richardson & Cane 2011a). FDs caused by high-speed streams (HSS) from coronal holes (CHs) have a recurrent character (Iucci et al. 1979b; Richardson 2004; Singh & Badruddin 2007b). The influence of different types of SW disturbances on galactic CR has been well-documented in scientific literature (e.g. Dumbovic et al. 2012; Chertok et al. 2013; Kumar & Baddruddin 2014a,b; Melkumyan et al., 2018, 2019). It is known that most of CMEs, and almost all energetic CMEs, are originated from the active regions being accompanied by solar flares which intensity correlated with the CME kinetic energy (e.g. Yashiro & Gopalswamy 2009). Comparison between the impact on the heliosphere of active-region (AR) and non-AR CMEs is presented in Gopalswamy et al. (2010b). The authors reveal that: (i) active regions produce almost all CMEs with above-average energy, for example geoeffective CMEs, CMEs associated with solar energetic particles, shock-driving CMEs; (ii) the quiescent filament regions produce the other type of CMEs which are not related to the sunspots and occur mostly at high latitudes during the maximum phase of solar activity. One of the factors responsible for the variability of FDs is different origin of related interplanetary transients. In Marii et al. (2020, 2021) the influences of different parts of ICMEs (turbulent sheath and magnetic obstacle) on the associated FDs are examined according to the CME origin. The events are separated into three subsets: AR CMEs, disappearing filament CMEs, and stealthy CMEs. CR variations caused by turbulent sheath and magnetic obstacle parts of the corresponding ICME are compared for the three subsets and two different linear relations are found: for AR ICMEs on one side and for filament/stealthy ICMEs on the other side.

2020MNRAS.492.3420B__Hildebrandt_et_al._2018_Instance_1

Given the potential huge information content of these observables advocated by some recent works (Patton et al. 2017), the predictions from first principles developed in this paper could be successfully applied to forthcoming data along with the standard power spectrum based analysis and could bring additional information beyond ΛCDM parameters like massive neutrinos (Liu & Madhavacheril 2019) or dark energy (Codis et al. 2016a). Let us stress that implementing nulling in weak-lensing analysis is central in order to avoid extracting biased information from the small scales that lack a full theoretical understanding (including due to the effect of baryon physics that needs to be modelled in weak-lensing surveys Hildebrandt et al. 2018; Yoon et al. 2019). Not only this general nulling technique should be used for one-point statistics but could also be applied to standard power spectrum analysis (and more generally to the full two-point PDF) in order to disentangle the effects of the different physical scales. However, more realistic effects have to be accounted for before the here mentioned formalism could be directly applied to real data. In particular, we have not investigated the precise impact of the galaxy redshift distribution ns(z) (for which one needs to go from a set of discrete source planes to a source distribution), photometric redshift errors or shape noise, which are left for future works. Promising extensions include an application of the formalism to (i) compensated filters such as for aperture mass which require the joint modelling of the field at two different scales, (ii) two-point statistics in order to model cosmic variance, and (iii) the joint analysis of multiple redshift bins. All of these ideas are within reach of LDT as was shown in the case of the three-dimensional matter density in Bernardeau, Codis & Pichon (2015) and Codis et al. (2016b) for, respectively, the multiscale and two-point statistics.

2019MNRAS.484.1645O__Hummel_et_al._1991_Instance_1

We justify our use of the Two-Zone approximation as follows: we anticipate that the magnetic structure within the outflow would be perpendicular to the plane of the host galaxy and thus perpendicular to the magnetic field orientation within the Zone B ISM region. Indeed, such perpendicular magnetic structure in outflows is seen in simulation work where, e.g. the action of a CR-driven dynamo yields a perpendicular magnetic field configuration compared to the host galactic plane (Kulpa-Dybeł et al. 2011), or by the advection of the magnetic fields by the flows themselves (Bertone et al. 2005), by magnetic amplification via the CR streaming instability (Uhlig et al. 2012) along the outflow. This magnetic structure would also be consistent with polarised radio synchrotron emission above and below the planes of galaxies known to host outflows in the nearby Universe, with the polarisation direction aligned with the orientation of the outflow cone (see, e.g. Hummel et al. 1988; Sukumar & Allen 1990, 1991; Hummel et al. 1991; Brandenburg et al. 1993; Chyży et al. 2006; Soida et al. 2011; Mora & Krause 2013). We argue that the principle mechanism for CRs to permeate the Zone A/Zone B interface would be via diffusion. With magnetic field lines aligned in a direction parallel to the inter-zone boundary, diffusion across the interface would be severely hampered – the cross-boundary diffusion coefficient would effectively be perpendicular the the local magnetic field lines, and so would be around two orders of magnitude smaller than that along the field directions (e.g. Shalchi et al. 2004, 2006; Hussein & Shalchi 2014), and substantially less than the effective ISM diffusion coefficient. The detailed substructure of the magnetic fields in these interfacing regions is not yet fully understood (Veilleux et al. 2005), but we argue that our prescription is consistent with existing work on relevant scales and that adopting an alternative model for CR transport across this boundary at this point would not imply an interpretation that is any more physical than that adopted here. We acknowledge that, in future studies, it will be critical to assess the magnetic fields in these interfacing regions across a range of length-scales to properly determine the permeability of the Zone A/Zone B interface to diffusing CRs.

2017MNRAS.469.3322G__Narayan_&_Yi_1994_Instance_1

The induction equation of the magnetic field is (10) \begin{equation} \frac{\partial \boldsymbol {B}}{\partial t}={\nabla }\times \left(\boldsymbol {v}\times \boldsymbol {B}-\frac{4\pi }{c}\eta \boldsymbol {J}\right), \end{equation} where $\boldsymbol {J}=\frac{c}{4\pi }{\nabla }\times \boldsymbol {B}$ is the current density. The induction equation is the field escaping/creating rate due to magnetic instability or the dynamo effect. For the steady-state accretion flow, we neglect the dynamo effect. In the energy equation, we assumed a balance between the heating due to viscosity and cooling due to advection, convection and radiation as (11) \begin{equation} Q^{-}_{{\rm adv}}+Q^{-}_{{\rm rad}}+Q^{-}_{{\rm con}}=Q^{+}_{{\rm diss}}, \end{equation} where $Q^{-}_{{\rm adv}}=\scriptstyle\rho (\frac{{\rm d} e}{{\rm d}t}-\frac{p}{\rho ^{2}}\frac{{\rm d}\rho }{{\rm d}t})$ is advection cooling in ADAFs. e is the gas internal energy $(e=\frac{c^{2}_{{\rm s}}}{\gamma -1})$ and γ is the specific heat ratio. Also, we consider $Q^{+}_{{\rm diss}}-Q^{-}_{{\rm rad}}=fQ^{+}_{{\rm diss}}$ in ADAFs (where f, the advection parameter, is defined by Narayan & Yi 1994). Also, the viscous heating is defined as $Q_{{\rm diss}}= f(\nu +\frac{1}{3}\nu _{{\rm con}})\Sigma r^{2} (\frac{{\rm d} \Omega }{{\rm d}r})^{2}$. The outward energy flow by convection is $Q^{-}_{{\rm con}}=- {\nabla }\cdot \boldsymbol {F}_{c}$, where $F_{{\rm con}}=-\nu _{{\rm con}} \frac{1}{\gamma -1}\frac{{\rm d} c^{2}_{{\rm s}}}{{\rm d}r}-\frac{c^{2}_{{\rm s}}}{\rho }\frac{{\rm d} \rho }{{\rm d}r}$. So, the energy equation is written by considering the above definitions and the specific internal energy of inflow ε and outflow εw on the surface of the disc as follows: (12) \begin{eqnarray} &&{\frac{\Sigma v_{r}}{\gamma -1}\frac{{\rm d} c^{2}_{{\rm s}}}{{\rm d}r}-2Hc^{2}_{{\rm s}}v_{r}\frac{{\rm d}\rho }{{\rm d}r}+\frac{1}{2\pi r}\frac{{\rm d}\dot{M}(r)}{{\rm d}r}(\epsilon _{w}-\epsilon )}\nonumber \\ &&-\frac{1}{r}\frac{{\rm d}}{{\rm d}r}\left(r\nu _{{\rm con}}\frac{\Sigma }{\gamma -1}\frac{{\rm d} c^{2}_{{\rm s}}}{{\rm d}r}-2Hr\nu _{m}c^{2}_{{\rm s}}\frac{{\rm d}\rho }{{\rm d}r}\right)\nonumber \\ && -\frac{\nu \rho }{\gamma -1}\frac{{\rm d} c^{2}_{{\rm s}}}{{\rm d}r}+\nu _{{\rm con}}c^{2}_{{\rm s}}\frac{{\rm d}\rho }{{\rm d}r} = f\Sigma \left(\alpha -\frac{1}{3}\alpha _{\rm c}\right) c_{{\rm s}}H r^{2}\left(\frac{{\rm d} \Omega }{{\rm d}r}\right)^{2}. \nonumber\\ \end{eqnarray}

2020AandA...639A..46B__Štverák_et_al._(2009)_Instance_1

The linear relationship that we observe between breakpoint energy and core temperature is in line with previous measurements (e.g. McComas et al. 1992; Štverák et al. 2009), for both the halo and strahl. According to Scudder & Olbert (1979), a linear trend in the halo relation also follows under the assumption that binary Coulomb collisions dominate electron dynamics in the solar wind. However, in order to align with available experimental data, Scudder & Olbert (1979) set a scaling factor of Ebp/kBTc = 7, which differs from our scaling factor of Ebp/kBTc = 5.5 ± 0.1. With a scaling factor of Ebp/kBTc = 7, Scudder & Olbert (1979) predict that a transformation of thermal electrons into the suprathermal population occurs as the solar wind flows out from the Sun. Findings by Štverák et al. (2009), on the other hand, show that the (nh + ns)/nc ratio remains roughly constant with heliocentric distance in the slow wind, suggesting a lack of interchange between the thermal and suprathermal populations. However Štverák et al. (2009) observes some variability in the (nh + ns)/nc ratio in the fast wind, which they attribute to either statistical effects due to a lack of samples or a possible “interplay” between thermal and suprathermal electrons. Scudder & Olbert (1979) also predict that the halo Ebp/kBTc ratio remains constant with heliocentric distance, whereas Štverák et al. (2009) find that the halo Ebp/kBTc ratio decreases with heliocentric distance. These findings by Štverák et al. (2009), along with the discrepancy between our calculated ratio of Ebp/kBTc = 5.5 ± 0.1 and the prediction of Ebp/kBTc = 7, suggest that the model of Scudder & Olbert (1979) requires a minor update to either the theory or to the input parameters. The discrepancy, however, may also be indicative of other processes, such as wave-particle scattering (e.g. Gary et al. 1994), that possibly modifies the ratio between breakpoint energy and core temperature while preserving its linear relationship.

2018MNRAS.479.4509R__Kingma_&_Ba_2014_Instance_1

After each step of calculations, the network should optimize the model based on its current and previous states to improve the subsequent mapping. Our model utilizes a computationally memory efficient optimization due to its dependence to only the first-order gradients, namely the ‘adaptive moment estimation’ (or Adam). For more details, we refer the readers to Kingma & Ba (2014). Adam optimization, compared to other gradient-based optimization, is very suitable for noisy and sparse gradients, and for simulated data that show very large scatter with respect to a given quantity of parameter (Kingma & Ba 2014). With this optimizer, we have to decide few parameters in advance. The learning step α and the parameters controlling the moving averages of the first- and second-order moments, namely β1 and β2 (both ∈[0,1)), respectively. For this purpose, we chose to minimize the MSE between the target and the prediction from the model: in what follows, we will alternatively call the MSE the ‘objective function’ f($\bf x$): with ${\bf x}$ the parameters of the model to be updated, such as weights and biases. At a given time t ≤ T, where T is the maximal learning time-step, we can update the parameters of the model as shown in the following: (11) \begin{eqnarray*} g_t &=\nabla _\mathrm{ \text{$x$}} f(\mathrm{\text{$x$}}_{t-1}), \end{eqnarray*} (12) \begin{eqnarray*} \mu _{1,t} &=\beta _1 \times \mu _{1,t-1} + (1-\beta _1)\times g_t, \end{eqnarray*} (13) \begin{eqnarray*} \bar{\mu }_{1,t} &=\mu _{1,t}/(1-\beta _1^t), \end{eqnarray*} (14) \begin{eqnarray*} \mu _{2,t} &=\beta _2 \times \mu _{2,t-1} + (1-\beta _2)\times g_t^2, \end{eqnarray*} (15) \begin{eqnarray*} \bar{\mu }_{2,t} &=\mu _{2,t}/(1-\beta _2^t), \end{eqnarray*} (16) \begin{eqnarray*} \mathrm{\text{$x$}}_t &=\mathrm{\text{$x$}}_{t-1} - \alpha _t \times \bar{\mu }_{1,t}/ (\sqrt{\bar{\mu }_{2,t}} + \epsilon), \end{eqnarray*} where $\alpha _t=\alpha \sqrt{1-\beta _2^t}/(1-\beta _1^t)$ is the time-step at t. Equation (11) shows the gradients of the objective function at t with respect to the model parameters. Equations (12) and (14) update the estimations of the first and second moments. Our moments are biased towards the initial values; thus, we require equations (13) and (15) to account for the corrections. Finally, we update the model parameters with equation (16).

2021MNRAS.504..146V__Vink_&_Gräfener_2012_Instance_1

The direct detection of the first gravitational waves from the merger of two heavy black holes (BHs) in GW 150914 confirmed one of the toughest predictions of Einstein’s theory of general relativity. But while satisfying the world of physics in general, for astrophysics this was only the beginning: many were surprised by the large BH masses of, respectively, 36 and 29  M⊙ (Abbott et al. 2016), showcasing how the new field of multimessenger astrophysics had just re-opened the field of stellar evolution in a spectacular fashion. Stellar mass BHs had previously been revealed by their interaction in binary systems (Orosz et al. 2011), but the maximum stellar BH mass in our Milky Way is not higher than roughly 15–20  M⊙ (Belczynski et al. 2010). While we know that very massive stars (VMS) above 100  M⊙ exist (Crowther et al. 2010; Vink et al. 2015), this mass is significantly diminished via stellar winds already during core hydrogen (H) burning (Vink & Gräfener 2012). The heavy nature of the BH, as measured by LIGO/VIRGO therefore supported the assumption that the gravitational wave event occurred in a part of the Universe still pristine in its enrichment with heavy elements (‘metallicity (Z)’), lowering stellar wind mass-loss (Vink, de Koter & Lamers 2001; Vink & de Koter 2005). A low-Z solution was widely accepted until the announcement of a 70  M⊙ BH in LB-1 (Liu et al. 2019), spurring stellar evolution theorists to avoid heavy mass-loss in the Milky Way (Belczynski et al. 2020; Groh et al. 2020), either by arbitrarily lowering the mass-loss rates of VMS – seemingly contradicting VMS mass-loss calibrations (Vink & Gräfener 2012) – or by invoking the presence of a strong dipolar surface magnetic field that could quench the wind (Petit et al. 2017). While such magnetic fields in some 5–10 per cent of massive OB stars do indeed exist, no B-fields have yet been detected in VMS (Bagnulo et al. 2020). The problem of the formation of a $70\, \mathrm{ M}_\odot$ BH in a solar metallicity environment apparently resolved itself when the spectral signatures of LB-1 were re-interpreted (Abdul-Masih et al. 2020; El-Badry & Quataert 2020).

2019AandA...622A.146M__Arribas_et_al._(2014)_Instance_1

Previous works (e.g. Holt et al. 2011; Arribas et al. 2014; Villar Martín et al. 2014, 2015) have found very high reddening and densities associated with ionised outflows in local objects (e.g. Hα/Hβ ∼ 4.91 and ne ≳ 1000 cm−3, Villar Martín et al. 2014). Concerning the reddening, although we find that the outflowing gas is generally less affected by dust extinction than the disc, the median value of the total distribution is significantly affected by dust (Hα/Hβ ∼ 4.16), with tails up to Hα/Hβ ≳ 6. Similarly, the outflow density of MAGNUM galaxies is higher than the values in the disc gas, but appears to be far lower than the values found by these authors. This could stem from the fact that the galaxies studied by Holt et al. (2011), Arribas et al. (2014) are local luminous or ultra-luminous infrared galaxies (U/LIRGs), and those of Villar Martín et al. (2014, 2015) are highly obscured Seyfert 2, thus sampling sources that are more gas and dust rich compared to our sample. However, our values are also lower than the outflow densities found in Perna et al. (2017) (ne ∼ 1200 cm−3), who targeted optically selected AGNs from the SDSS, and Förster Schreiber et al. (2018b), who presented a census of ionised gas outflows in high-z AGN with the KMOS3D survey (ne ∼ 1000 cm−3). A possible explanation could be related to the high quality of our MUSE data, which also allows us to detect the faint [S II] emission associated with lower density regions. If we calculate the median densities of the disc and outflow components, weighting for the [S II] line flux, we obtain higher values (ne ∼ 170 cm−3 and ne ∼ 815 cm−3, for disc and outflow, respectively). This shows that previous outflow density values from the literature could be biased towards higher ne because they are based only on the most luminous outflowing regions, characterised by a higher S/N. This could also mean that outflows at high-z could be far more extended than the values we observe.

2019AandA...628L...2C__Cukanovaite_et_al._(2018)_Instance_1

As a final note, we point out that correcting for the effect of neutrinos did not change the finding that KIC 08626021 has a very thin helium-pure layer on top of its envelope. Such a thin layer might be explained by the presence of any mechanism competing against the separation of He, C, and O in the envelopes of DO and hot DB white dwarfs. For instance, Fontaine & Brassard (2005; see also Brassard et al. 2007) postulated the existence of a weak stellar wind of the order of ∼2 × 10−13M⊙ yr−1 in order to account for the presence of traces of C in Teff ∼ 20 000–30 000 K DB white dwarfs. Such a wind, similar to the solar wind, would be driven by deposition of acoustic energy generated by strong convective motions which characterize the outer layers of these stars. Perhaps a more likely mechanism is convective overshooting, which was first studied by Freytag et al. (1996) in a white dwarf context and more recently by Tremblay et al. (2015) and Cukanovaite et al. (2018) through detailed 3D simulations. Brassard & Fontaine (2015) explicitly used the overshooting prescription of Freytag et al. to demonstrate that element separation is indeed considerably slowed down in cooling hot white dwarfs. They found that only a “thin” layer of pure He (logq1 ∼ −8) has accumulated at the surface of their evolving model by the time the latter has cooled down to Teff ∼ 31 000 K. This is in line with our seismic inference for KIC 08626021. In addition, the new solution for KIC 08626021 does not change the fact that no C/O/He triple transition at the core boundary is found in the model that best reproduces the pulsation frequencies of this star. If puzzling from an evolutionary perspective, our seismic probing seemingly requires that mode trapping generated by two chemical transitions around logq ∼ −2.4 and logq ∼ −3.5 must be present. This can be obtained with our current models only if a carbon buffer exists between the envelope and the core. Alternately, enforcing a triple transition and searching for a new optimal solution within this additional constraint strongly degrades the quality of the achieved best frequency match. Improving the capacity of such a configuration to reproduce the observed pulsation modes would likely require the presence of an additional structure (of unclear origin) in the envelope to provide the needed mode trapping. We therefore leave that issue open at this stage.

2018AandA...618A.145O__Brouillet_et_al._2013_Instance_1

Such a chemical differentiation has been reported in other multiple systems, like the low-mass protostars IRAS16293-2422 (2004a; 2016; 2011) and IRAS4A (Santangelo et al. 2015; López-Sepulcre et al. 2017), and, recently, towards the intermediate-mass protostars NGC 2264 CMM3 (Watanabe et al. 2017). With four examples at hand, we speculate that it could be a general feature of multiple protostellar systems and not a “pathological anomaly”. There is no systematic trend between the millimetre thermal dust and molecular line emissions. Towards IRAS16293-2422 and IRAS4A, a rich content in COMs is observed towards the source with the less massive continuum source. Towards NGC 2264 CMM3, it is the most massive continuum component that displays a rich molecular content. The case of Cep E-mm appears similar to the latter one. High-mass star forming regions (HMSFRs) also present a rich chemical diversity. One of the best known examples is provided by Orion-KL. This source harbours: (i) a dichotomy between the spatial distribution of complex O-bearing and complex N-bearing species, with the latter species probing the hotter gas (see, e.g. Guélin et al. 2008; Favre et al. 2011; Friedel & Widicus Weaver 2012; Peng et al. 2013; Brouillet et al. 2013; Crockett et al. 2014, 2015) but also (ii) differences between supposed chemically related species (see, e.g. Favre et al. 2017; Pagani et al. 2017). Other examples are provided by W3(OH) (Qin et al. 2015; Nishimura et al. 2017) and SgrB2 (Belloche et al. 2008, 2013). In an observational study of four HMSFRs (Orion KL, G29.96, IRAS 23151+5912, and IRAS 05358+3543), Beuther et al. (2009) showed that the properties of CH3OH can be easily accounted for by the physical conditions (temperature) in the cores, whereas the N-bearing species appear to be more selective as they are detected only towards the sources at the (evolved) hot core stage. Recently, in an ALMA study of the filamentary HMSFR G35.20, Sánchez-Monge et al. (2014) found that only three out of the six continuum cores of the filament display COM emission typical of hot cores. Several hypotheses have been proposed to account for the observed chemical differentiation. López-Sepulcre et al. (2017) proposed that the COM-rich protostar is either more massive and/or subject to a higher accretion rate, resulting in a lower envelope mass. Watanabe et al. (2017) suggest that the less massive protostar is related to a younger evolutionary stage in which the hot corino (hot core) is not yet developed, meaning that its dimensions are still very small. In Cep E-mm, the presence of high-velocity SiO jets provides evidence of active mass ejection around both protostars. The short dynamical timescales (500–1000 yr) also indicate that these ejections began recently, meaning that both sources are still in an early evolutionary stage. Incidentally, Lykke et al. (2015) found an apparent correlation between the source luminosities and the relative abundance of complex organic molecules in a sample of sources including high-mass protostars. The authors have suggested that this could be the result of the timescale and the temperature experienced by a source during its evolution. The sample of sources with evidence for chemical differentiation should be increased in order to confirm this observational trend.

2015AandA...584A..75V__Essen_et_al._(2014)_Instance_5

The data presented here comprise quasi-simultaneous observations during secondary eclipse of WASP-33 b around the V and Y bands. The predicted planet-star flux ratio in the V-band is 0.2 ppt, four times lower than the accuracy of our measurements. Therefore, we can neglect the planet imprint and use this band to measure the stellar pulsations, and most specifically to tune their current phases (see phase shifts in von Essen et al. 2014). Particularly, our model for the light contribution of the stellar pulsations consists of eight sinusoidal pulsation frequencies with corresponding amplitudes and phases. Hence, to reduce the number of 24 free parameters and the values they can take, we use prior knowledge about the pulsation spectrum of the star that was acquired during von Essen et al. (2014). As the frequency resolution is 1/ΔT (Kurtz 1983), 3.5 h of data are not sufficient to determine the pulsations frequencies. Therefore, during our fitting procedure we use the frequencies determined in von Essen et al. (2014) as starting values plus their derived errors as Gaussian priors. As pointed out in von Essen et al. (2014), we found clear evidences of pulsation phase variability with a maximum change of 2 × 10-3 c/d. In other words, as an example after one year time a phase-constant model would appear to have the correct shape with respect to the pulsation pattern of the star, but shifted several minutes in time. To account for this, the eight phases were considered as fitting parameters. The von Essen et al. (2014) photometric follow-up started in August, 2010, and ended in October, 2012, coinciding with these LBT data. We then used the phases determined in von Essen et al. (2014) during our last observing season as starting values, and we restricted them to the limiting cases derived in Sect. 3.5 of von Essen et al. (2014), rather than allowing them to take arbitrary values. The pulsation amplitudes in δ Scuti stars are expected to be wavelength-dependent (see e.g. Daszyńska-Daszkiewicz 2008). Our follow-up campaign and these data were acquired in the blue wavelength range. Therefore the amplitudes estimated in von Essen et al. (2014), listed in Table 1, are used in this work as fixed values. This approach would be incorrect if the pulsation amplitudes would be significantly variable (see e.g., Breger et al. 2005). Nonetheless, the short time span of LBT data, and the achieved photometric precision compared to the intrinsically low values of WASP-33’s amplitudes, make the detection of any amplitude variability impossible.

2016AandA...588A..25M__Vassiliadis_&_Wood_(1994)_Instance_1

The faster evolutionary timescales and higher luminosities of our H-burning sequences should have an important impact on the study of the formation of PNe (Schönberner et al. 2014; Toalá & Arthur 2014). The low-mass models of Schönberner (1983), which are still in use to complement the sequences of Blöcker (1995a), show crossing timescales of ~340 kyr (0.546 M⊙) and ~20 kyr (0.565 M⊙). Our H-burning sequences of similar mass and metallicity (Z0 = 0.02) show crossing timescales about ~3.5 to ≳15 times faster and even faster in the case of M15 models. The discrepancy is even larger in the case of the low-mass models of Vassiliadis & Wood (1994). In order to be able to produce PNe, the central stars need to evolve in less than a few tens of thousand years. If that is not the case, the circumstellar material dissipates before the star becomes hot enough to ionize it. This fact, together with the very long timescales of τcross ≳ 100 kyr of the low-mass models of older grids, leads to the conventional wisdom that low-mass post-AGB stars (≲0.55 M⊙) cannot form PNe; see, e.g., Jacoby et al. (2013), Bond (2015). In this context, the new models might help to explain the existence of single CSPNe with masses lower than ~0.55 M⊙ (Althaus et al. 2010; Werner & Rauch, in prep.). The new models should also have an impact on the question of whether single stellar evolution can form PNe in globular clusters (Jacoby et al. 2013; Bond 2015). Our H-burning post-AGB sequences with ages similar to that of globular clusters (9 to 12 Gyr) have values of τcross ~ 25–70 kyr. Timescales drop to τcross ~ 5–10 kyr for post-AGB sequences with slightly younger progenitors (5 to 7 Gyr); see Tables 2 and 3. Timescales are even shorter in the case of the models of Weiss & Ferguson (2009) and M15 (see Appendix B). The much shorter timescales of the new H-burning post-AGB sequences call into question the idea that single stellar evolution cannot produce PNe in globular clusters.

2018MNRAS.481.5630S__Tody_1993_Instance_1

We also introduce here NB2071 data, taken as part of the MDCS with the Multi-Object Infrared Camera and Spectrograph (MOIRCS; Ichikawa et al. 2006; Suzuki et al. 2008) on the Subaru Telescope (the same instrument that was used in the past MAHALO-Subaru survey; Koyama et al. 2013a). The observations were executed between April 30 and May 6, 2015, under photometric conditions with seeing FWHM ∼0.6 arcsec. The integration time is 125 min which was split into 180 s individual exposures. After combining with the existing NB2071 data (186 min integration), we reconstructed all the data using the reduction pipeline mcsred2 (Tanaka et al. 2011), which is written as iraf3 scripts (Tody 1993). As described in Shimakawa et al. (2018), we executed flat-fielding, masking objects from the combined data in the first run (thus the whole reduction process was conducted twice to remake secure object masks), sky subtraction (by median sky and then the polynomially fitted plane for residual sky subtraction), distortion correction, cross-matching, and image mosaicing with this pipeline. The reconstructed NB2071 image reaches 23.95 mag in 3σ limiting magnitude using a 1.4 arcsec diameter aperture, and its seeing FWHM is 0.63 arcsec. The image depth becomes deeper by 0.5 mag than the previous data (Koyama et al. 2013a). The world coordinate system (WCS, Calabretta & Greisen 2002; Greisen & Calabretta 2002) of the narrow-band image is carefully matched by the iraf scripts (ccmap and ccsetwcs) to that of the F814W image, based on 67 point sources. F814W has one of the best spatial resolutions amongst our data set. The standard deviation of point source separations between the NB2071 and F814 images suggests that the relative WCS uncertainty would be around 0.04 arcsec in the survey area. One should note, however, that the absolute astrometry would have 0.3 arcsec errors in right ascension and declination based on comparison with the Guide Star Catalogue 2 (Lasker et al. 2008).

2019AandA...626A..34C__Ohba_et_al._2005_Instance_1

The uncertainty for most frequency determinations in the spectrum of NEFA is of a few kHz in view of the high S/N of the data (see Fig. 20). However, we have assigned an uncertainty of 10 kHz for all lines showing a single component and intensity larger than 20 mK (S/N ≥ 20). Unlike linear molecules with a nitrogen atom, for which hyperfine splittings rapidly collapse, symmetric or asymmetric tops can show significant hyperfine splitting even for high-J and high-Ka transitions. As an example, the bottom panel of Fig. 10 shows the hyperfine structure of K = 3 component of the J = 6 → 5 rotational transition of CH3CN. The K = 0 and 1 components of the same transition do not show any measurable splitting, while the K = 2 exhibits an emerging shoulder (clearly seen in that figure) due to its hyperfine structure. Using the diagonal elements of the 14N quadrupole coupling tensor in NEFA (Ohba et al. 2005), we have computed the expected hyperfine splitting of all lines in the W band. Most of the lines we have identified have the hyperfine splitting concentrated around ±10 kHz of the central frequency. However, several lines present three hyperfine components with similar intensities. Two of these components are at practically the same frequency, while the third component is always on the opposite side producing a global splitting of 60–100 kHz. Hence, some of the observed lines could have a two-component structure, with intensity ratio 1:2, with a strong feature accompanied by less intense shoulder (a factor of two lower), either left or right of the central frequency. Many other lines also show a complex profile due to blending with lines coming from the vibrationally excited states and the other conformers of NEFA (see, for example, the bottom right panel of Fig. 20). We have fitted in most cases one single Gaussian profile to the observed lines. However, when the line profile exhibits two components we fitted two Gaussians. Depending on the degree of overlap between these components, the assigned uncertainty varies between 20 and 80 kHz. When two components were obvious in the line profile, the feature assigned to NEFA was based on the expected intensity of the transition (determined with the MADEX code; Cernicharo 2012), and by comparison with the intensity of other nearby lines of NEFA.

2019MNRAS.486.1781R__Gu_et_al._2006_Instance_1

To check for any spectral variation in the optical/IR bands, we looked for variation in the V − J band colour against the V-band brightness. This colour variation was analysed for the epochs A, B, D, and E. During epochs A and B, the source showed a ‘redder when brighter’ (RWB) behaviour. During epoch E, a bluer when brighter behaviour was observed. During epoch D, we observed a complex behaviour. Upto a V-band brightness of around 15 mag, the source showed a ‘bluer when brighter’ behaviour, but for optical brightness fainter than 15.0 mag, a ‘redder when brighter’ behaviour was observed. The colour–magnitude diagrams for all the four epochs are shown in Fig. 10. The spectral variations shown by the source are thus complex. From studies on the optical–IR colour–magnitude diagram, it is known that FSRQs in general show an RWB trend, which is attributed to them having a luminous accretion disc (Gu et al. 2006; Bonning et al. 2012). The observed optical emission is a combination of thermal blue emission from the accretion disc and non-thermal red emission from the jet. As the source gets brighter, the non-thermal emission has a more dominant contribution to the total flux, giving rise to the RWB behaviour (Bonning et al. 2012). During epochs A and B, there is a trend of the object to become RWB, irrespective of its optical brightness. The optical flares dominated by synchrotron emission processes during A and B have corresponding γ-ray flares that are produced by EC processes. However, during epochs D and E, the colour variations were found to depend on the optical brightness. During the epochs when this complex spectral behaviour was noticed, the source showed an optical/IR flare with no or a weak corresponding flare in the γ-ray band. The source showed a much larger amplitude of variability in the optical/IR bands, while in the γ-ray band it was either faint or below the detection limit of Fermi. This definitely points to some complex physical changes and could be due to a combination of changes in the bulk Lorentz factor, electron energy density, and magnetic field as seen from our SED modelling of the multiband data.

2016ApJ...825...10T___2009_Instance_1

Although this penalty is small, we can still provide a quantitative estimate based on a few assumptions. The observations of M15 that measured the parallax to VLA J2130+12 (K14) employed an interferometric technique where the position of a weaker potential in-beam calibrator source (M15 S1 and VLA J2130+12) can be measured against a brighter primary calibration source, with the potential for using the in-beam calibrator to transfer more accurate calibration solutions to other in-beam targets. Based on a literature review for papers where VLBI parallax measurements were determined using an in-beam calibrator, we find 24 similar measurements, the majority of which have been used to measure parallaxes to pulsars (Fomalont et al. 1999; Brisken et al. 2003; Chatterjee et al. 2005, 2009; Ng et al. 2007; Middelberg et al. 2011; Deller et al. 2012, 2013; Ransom et al. 2014; Liu et al. 2016). As these measurements represent additional opportunities to detect the parallax of a VLA J2130+12-like object, they could be considered as potential trials. However, all of the reported in-beam calibrators were significantly brighter (4–86 mJy) than VLA J2130+12 (∼0.1–0.5 mJy). Given our conservative assumption of a uniform volume density of VLA J2130+12-like objects in the Galaxy, we should down-weight the number of trials from brighter sources by (fν,in-beam/fν,VLA J2130+12)−1.5. In that case, the number of trials penalty is only an additional ∼0.2 trials. In addition, we note that the PSRπ parallax project (a large VLBA program) has reported15 15 https://safe.nrao.edu/vlba/psrpi/ 111 additional in-beam calibrator sources that they used to measure parallaxes. Although they do not provide the flux densities of individual sources, they note that their median in-beam calibrator source is 9.2 mJy. We have measured the flux density function of secure 1–20 mJy FIRST sources (Helfand et al. 2015; ) to estimate the expected distribution of the flux densities of in-beam calibrators. We found that the minimum flux density is likely ∼3.2 mJy, and using the same down-weighting we estimate an additional penalty of ∼0.9 trials.

2020MNRAS.497.4293G__Ferguson_et_al._2004_Instance_1

Recently Izotov et al. (2016a,b, 2018a,b) have discovered significant emission of Lyman continuum (LyC) ionizing radiation leaking with the escape fractions of 2–76 per cent in a sample of 11 low-z compact active star-forming galaxies (SFGs) observed with the Hubble Space Telescope (HST) in conjunction with the Cosmic Origins Spectrograph (COS). These galaxies hereafter referred to as LyC leakers, possess many properties similar to those of high-redshift galaxies both at z ∼ 2–3 and z$\gtrsim$ 6 such as compact morphology with similar galaxy radii (e.g. Bouwens et al. 2004; Ferguson et al. 2004; Oesch et al. 2010; Ono et al. 2012; Shibuya, Ouchi & Harikane 2015; Curtis-Lake et al. 2016; Paulino-Afonso, Sobral & Ribeiro 2018), strong emission lines with high equivalent widths (EWs; e.g. Schaerer & de Barros 2009; Smit et al. 2014, 2015; Roberts-Borsani et al. 2016; Bowler et al. 2017; Castellano et al. 2017; Fletcher et al. 2019; Bian & Fan 2020; Endsley et al. 2020), similar low stellar masses, low metallicities, and high specific star formation rates (SFRs; e.g. Jaskot & Oey 2013; Nakajima et al. 2013; Stark et al. 2013a; de Barros, Schaerer & Stark 2014; Duncan et al. 2014; González et al. 2014; Nakajima & Ouchi 2014; Becker, Bolton & Lidz 2015; Grazian et al. 2015; Salmon et al. 2015; Huang et al. 2016; Stark 2016; Santini et al. 2017; Stark et al. 2017; Dors et al. 2018), small dust content (e.g. Ouchi et al. 2013; Ota et al. 2014; Maiolino et al. 2015; Schaerer et al. 2015; Watson et al. 2015), and are considered as the main sources of reionization of the Universe after the cosmic ‘Dark Ages’. This makes low-z LyC leakers the best local analogues of reionization galaxies (see e.g. Schaerer et al. 2016; Stark 2016; Ma et al. 2020). Given their proximity, these galaxies represent excellent laboratories for a detailed study of their physical conditions, and the main mechanisms responsible for LyC leakage. Ground-based spectroscopic observations in the visible and near-infrared ranges are necessary for that.

2019AandA...629A..95S___2017a_Instance_1

A rough idea of the location of the inert Oort cloud can be obtained from previous works. Gladman et al. (2002) showed that the scattering effect by Neptune is significant over long timescales only for perihelion distances below about 45 astronomical units (au). The precise limit actually increases with the semi-major axis (Gallardo et al. 2012), because energy kicks result in larger variations of semi-major axis if the semi-major axis is large. In fact, some observed objects with perihelion beyond 45 au are known to experience scattering (Bannister et al. 2017). In any case, we look here for a rough limit only. The scattering process mostly affects the semi-major axis of small bodies, which diffuses chaotically, while the perihelion distance does not vary much. Later on, Gomes et al. (2005), Gallardo et al. (2012), and Saillenfest et al. (2016, 2017a), showed that the Lidov–Kozai mechanism raised by the giantplanets inside a mean-motion resonance with Neptune is able to raise the perihelion of small bodies beyond 60 au in a few thousands of million years. Contrary to scattering effects, this mechanism induces a variation of perihelion distance and inclination, while the semi-major axis remains at the resonance location. This mechanism, however, is only efficient for semi-major axes smaller than about 500 au. From these studies, one can deduce that the action of the planets is limited to orbits with perihelion distances smaller than about 80 au, and that for perihelion beyond 45 au, the semi-major axis should be smaller than 500 au for the planets to possibly have a substantial effect through mean-motion resonances. As regards the effects of the galactic tides, Fouchard et al. (2017) showed that an object with perihelion in the Jupiter-Saturn region, that is, below 15 au from the Sun, should have a semi-major axis larger than 1600 au for the tides to be able to raise its perihelion beyond 45 au in less than the age of the solar system. In other words, the tides can move its perihelion out of reach of any significant planetary scattering.

2021ApJ...908...40M__Muschietti_&_Lembège_2017_Instance_2

These signatures are inconsistent with ultra-low frequency waves, which have circular polarization and a period similar to the upstream ion gyroperiod. The waves are also inconsistent with ion Weibel instability, which generates linearly polarized waves. Interaction of reflected ions with incoming solar wind electrons or ions can cause foot instabilities that excite waves in the whistler mode branch. Modified Two Stream Instability (MTSI) due to relative drift between reflected ions and incoming solar wind electrons (fast drift), and incoming solar wind ions and electrons (slow drift) has been frequently considered (Matsukiyo & Scholer 2003; Comişel et al. 2011; Umeda et al. 2012; Marcowith et al. 2016; Wilson et al. 2016; Muschietti & Lembège 2017; Hull et al. 2020). This instability, however, if excited, creates significant ion heating throughout the foot and suppresses the reformation process (Shimada & Hoshino 2005; Matsukiyo & Scholer 2006), rather than creating episodic enhancements that we show in the foot. Furthermore, Gary et al. (1987) indicated that (fast drift) MTSI becomes dominant at low electron beta (βe 0.5), while at higher βe more resonant electrons stabilize this instability through increased electron Landau damping. Electron data for the time period we discussed in this paper show βe ≥ 1.2, and therefore fast drift mode MTSI is most likely not significant. The slow drift mode of MTSI could be a more viable candidate at high β plasmas. Wave properties around 1.6 Hz in the middle interval (purple segment) of Figure 6, indicate that the wave is in propagation toward the ramp ( = −0.66, −0.71, 0.22) with Vph‐sc = 34 km s−1 and λwave = 21.4 km ∼ 12λe, where λe is the upstream electron inertial length. The plasma rest-frame frequency of the wave is about 8 Hz ∼ 2.5flh. Since these characteristics are somewhat consistent with model predictions for drift mode of MTSI (Muschietti & Lembège 2017), we do not rule out the possibility of some waves at certain frequencies and during some intervals being generated by the slow drift mode of MTSI.

2015AandA...577A.117S__Kaluzny_et_al._(2013a)_Instance_1

A lower CB frequency in GCs than in the field has already been noticed by Rucinski (2000). It results most likely from a very low binary frequency of ~0.1 in GCs (but with a significant scatter among different clusters), compared to 0.5 in the field (Milone et al. 2012), together with a low percentage of binaries in the contact phase at a given age, as shown above. Any statistical comparison of models with observations is therefore extremely uncertain because we have so few observational data. Nevertheless, we can compare the predicted to observed fraction of BSs among all CBs in a GC. We used data for two clusters observed by Kaluzny and his collaborators with the highest number of CBs (except for ω Centauri). In M4, Kaluzny et al. (2013a) detected nine CBs with one BS among them, and in 47 Tuc, Kaluzny et al. (2013b) identified 15 CBs or NCBs, of which six are BS. The resulting fractions are 0.1 and 0.4, respectively. We did not take into account ω Cen with the richest population of CBs (Kaluzny et al. 2004) because it is highly atypical and the accurate number of member CBs is not well known, although the approximate data indicate a similarly high ratio as in case of 47 Tuc. The predicted fractions range from 0.08 to 0.2 for the three considered cluster ages. As we see, they are close to the lower observed value but are at odds with the higher value. This may indicate some deficiencies of the CCBM, for instance, too short initial cut-off period, a too high AML rate at short periods, or a too low mass-transfer rate. With the lower AML rate and/or higher mass transfer rate, a binary stays longer in contact and can reach a lower mass ratio before merging. Many of the short-period CBs have a rather high mass ratio of about 0.7−0.8 at the time of merging, whereas field CBs with P ≲ 0.3 d center around a value of 0.5 (Rucinski 2010; Stȩpień & Gazeas 2012). The lower mass ratio means more mass transferred to the gainer, hence its higher mass and higher position on the HRD, that is, a higher probability for entering the BS region. There may also still be another explanation of the discrepancy: strong fluctuations of this fraction among different clusters suggest a nonuniform BSs formation rate in some clusters, with individual bursts occurring in the recent past (see below). Regardless of the reason, it is apparent that profound differences occur among GCs, which makes a comparison of theoretical predictions with individual clusters uncertain.

2019AandA...623A.140G__Pohl_et_al._2017_Instance_2

HD 169142 is a very young Herbig Ae-Be star with a mass of 1.65–2 M⊙ and an age of 5–11 Myr (Blondel & Djie 2006; Manoj et al. 2007) that is surrounded by a gas-rich disk (i = 13°; Raman et al. 2006; PA = 5°; Fedele et al. 2017) that is seen almost face-on. The parallax is 8.77 ± 0.06 mas (Gaia DR2 2018). Disk structures dominate the inner regions around HD 169142 (see, e.g., Ligi et al. 2018). Figure 1 shows the view obtained from polarimetric observations: the left panel shows the QΦ image in the J band obtained by Pohl et al. (2017) using SPHERE on a linear scale, and the two rings are clearly visible. The right panel shows a pseudo-ADI image of the inner regions obtained by differentiating the QΦ image (see Ligi et al. 2018, for more details). Biller et al. (2014) and Reggiani et al. (2014) discussed the possible presence of a point source candidate at small separation (0.2 arcsec from the star). However, the analysis by Ligi et al. (2018) based on SPHERE data does not support or refute these claims; in particular, they suggested that the candidate identified by Biller et al. (2014) might be a disk feature rather than a planet. Polarimetricimages with the adaptive optics system NACO at the Very Large Telescope (VLT; Quanz et al. 2013b), SPHERE (Pohl et al. 2017; Bertrang et al. 2018) and GPI (Monnier et al. 2017) show a gap at around 36 au, with an outer ring at a separation >40 au from the star. This agrees very well with the position of the rings obtained from ALMA data (Fedele et al. 2017); similar results were obtained from VLA data (Osorio et al. 2014; Macías et al. 2017). We summarize this information about the disk structure in Table 1 and call the ring at 0.17–0.28 arcsec from the star Ring 1 and the ring at 0.48–0.64 arcsec Ring 2. We remark that in addition to these two rings, both the spectral energy distribution (Wagner et al. 2015) and interferometric observations (Lazareff et al. 2017; Chen et al. 2018) show an inner disk at a separation smaller than 3 au. This inner disk isunresolved from the star in high-contrast images and consistent with ongoing accretion from it onto the young central star.While the cavities between the rings seem devoid of small dust, some gas is present there (Osorio et al. 2014; Macías et al. 2017; Fedele et al. 2017). Fedele et al. (2017) and Bertrang et al. (2018) have suggested the possibility that the gap between Rings 1 and 2 is caused by a planet with a mass slightly higher than that of Jupiter. However, this planet has not yet been observed, possibly because it is at the limit of or beyond current capabilities of high-contrast imagers. On the other hand, Bertrang et al. (2018) found a radial gap in Ring 1 at PA ~ 50° that might correspond to a similar radial gap found by Quanz et al. (2013b) at PA ~ 80°. The authors noted that if this correspondence were real, then this gap might be caused by a planet at about 0.14 arcsec from the star. So far, this planet has not been unambiguously detected either.

2022MNRAS.516.2597S__Padmanabhan_2002_Instance_1

Observational data from distant Type Ia supernovae (SNIa) revealed a hidden fact that the present Universe is expanding in an accelerated phase (Riess et al. 1998; Perlmutter et al. 1999; Kowalski et al. 2008). This important fact has been confirmed by other cosmological observations such as the cosmic microwave background (CMB) (Komatsu et al. 2009; Jarosik et al. 2011; Ade et al. 2016), large-scale structure (LSS), and baryonic acoustic oscillation (BAO) (Tegmark et al. 2004; Cole et al. 2005; Eisenstein et al. 2005; Percival et al. 2010; Blake et al. 2011; Reid et al. 2012), high-redshift galaxies (Alcaniz 2004), high-redshift galaxy clusters (Wang & Steinhardt 1998; Allen et al. 2004), and weak gravitational lensing (Benjamin et al. 2007; Amendola, Kunz & Sapone 2008; Fu et al. 2008). The accelerated phase of the expansion can be interpreted by modifying the standard theory of gravity on cosmological scales or by adding an exotic cosmic fluid with negative pressure, the so-called dark energy (DE) (Riess et al. 1998; Perlmutter et al. 1999; Kowalski et al. 2008). Historically, Einstein’s cosmological constant Λ with a constant equation of state (EoS) parameter equal to −1 is the first and simplest DE model to interpret the current accelerated phase of the expansion. The standard model of cosmology, the ΛCDM model, which accounts for about 70 per cent of the total energy density of the Universe from Λ and about 30 per cent from cold dark matter (CDM) is consistent with the most of the cosmological data. From a theoretical point of view, however, this model has two fundamental problems, namely the problem of fine-tuning and the problem of cosmic coincidence (Weinberg 1989; Sahni & Starobinsky 2000; Carroll 2001; Padmanabhan 2003; Copeland, Sami & Tsujikawa 2006). In addition, the standard model of cosmology suffers from the big tension between the local measurement values of Hubble constant H0 with that of the Planck CMB estimation. Moreover, there are big tensions between the Planck CMB data with weak lensing measurements and redshift surveys, concerning the value of non-relativistic matter density Ωm and the amplitude of the growth of perturbations σ8. The above observational tensions can make the standard model as an approximation of a general gravitational scenario yet to be found. For a recent review about the cosmological tensions of the standard model, we refer the reader to see (Abdalla et al. 2022). In this concern, a large family of dynamical DE models with time-varying EoS parameters has been proposed in the literature (for some earlier attemts, see Armendariz-Picon, Mukhanov & Steinhardt 2000; Caldwell 2002; Padmanabhan 2002; Elizalde, Nojiri & Odintsov 2004). Parallel to the solution of DE, the positive cosmic acceleration can be seen as the expression of a new theory of gravity on large cosmological scales. Indeed, modifying the standard Einstein–Hilbert action in the context of the Friedmann–Robertson–Walker (FRW) metric leads to the modified Friedmann equations, which can be used to justify the current accelerated expansion of the Universe without resorting to DE fluid. One of the most popular modified gravity theories is the f(R) scenario, in which the Lagrangian of the modified Einstein–Hilbert action is extended to the function of the Ricci scalar R (Capozziello, Stabile & Troisi 2007; Capozziello & Francaviglia 2008; Sotiriou & Faraoni 2010; Nojiri & Odintsov 2011). Besides the f(R) theories of gravity, the so-called f(T) theory of gravity is the other solution to solve the puzzle of cosmic acceleration. This theory is defined on the basis of the old definition of the teleparallel equivalent of general relativity (TEGR), first introduced by Einstein (Einstein 1928) and extended by (Hayashi & Shirafuji 1979; Maluf 1994). A comprehensive study on the theory of teleparallel gravity (TG) can be found in the recent review by (Bahamonde et al. 2021). In general, an extended theory of gravity involves curvature, torsion, and non-metricity components. If only torsion is non-vanishing, one can obtain the torsional teleparallel geometry which is the basic geometry for the most f(T) theories in literature. TG theory can be used to formulate a TEGR formalism, which is dynamically equivalent to GR but may have different behaviors for other scenarios, such as quantum gravity. The Horndeski gravity can also be formulated using the teleparallel geometry as a possible revival modes for regular Hordenski gravity models (see Bahamonde et al. 2021, for more detils). TG as a theory built on the tangent sapce must be invariant under general coordinate transformation and local lorentz transformation. In the first formulation of teleparallel theories, it was assumed that the spin connection was always zero and then torsion tensor depends on the tetrads. This torsion tensor is a particular case which is computed in the so-called Weitzenböck gauge (see section 2.2.3 of Bahamonde et al. 2021). By taking a local Lorentz transformation only in the tetrads, one can conclude that the torsion tensor is non-covariant quantity under the local Lorentz transformation. In this context, the action of TEGR has a total divergence term which can be removed as being a boundary term. Hence, the TEGR action is invariant under local Lorentz transformation up to a boundary term. In modified teleparallel theories of gravity like f(T) gravity, we have no boundary term anymore, meaning that f(T) gravity models break the local Lorentz invariance. Notice that the problem of breaking the local Lorentz invariance is related to the particular Weitzenböck gauge (spin connection zero) (Krššák & Saridakis 2016). If we take the simultaneous transformations in the tetrads and the spin connection, the teleparallel theories are then fully invariant (diffeomorphisms and local Lorentz). Thus, any action and consequently field equations constructed based on the torsion tensor will be fully invariant. In the context of cosmology, there is a large body of works that has examined the cosmological properties of various f(T) models. In this framework, the dynamics and various aspects of the Universe with homogeneous and isotropic background are studied in recent works (Bengochea & Ferraro 2009; Linder 2010; Wu & Yu 2011, 2010b; Bamba et al. 2011, 2012; Dent, Dutta & Saridakis 2011; Geng et al. 2011; Myrzakulov 2011; Zhang et al. 2011). In general, cosmological consequences for the various formulations of TG at both background and perturbation levels have been discussed in (Bahamonde et al. 2021). The f(T) models have been studied and constrained using the various cosmological data (Wu & Yu 2010a; Capozziello, Luongo & Saridakis 2015; Iorio, Radicella & Ruggiero 2015; Nunes, Pan & Saridakis 2016; Saez-Gomez et al. 2016). As a more recent study, we refer the work of Briffa et al. 2022, where the various versions of f(T) model have been studied observationally. Using the combinations of cosmological data including the cosmic chronometers, the SNIa observations from Pantheon catalogue, BAO observations, and different model-independent values for Hubble constant H0, Briffa et al. (2022) studied a detailed analysis of the impact of the H0 priors on the late time properties of f(T) cosmologies. They found a higher value of H0 compared to equivalent analysis without considering H0 priors. In addition, they showed that the f(T) model produces the higher value of H0 and slightly lower value of matter density Ωm0 as compared to standard ΛCDM model. In general, studies on the cosmological tensions and their possible alleviation in TG formalism have been addressed in section 10 of review article (Bahamonde et al. 2021). For a review of cosmological tensions H0 and σ8, we refer the reader to see the current studies in (Verde, Treu & Riess 2019; Di Valentino et al. 2021a, b, c; Perivolaropoulos & Skara 2021).

2022ApJ...928..120G__Calzetti_et_al._2007_Instance_1

As part of this study, we investigate the 8 μm emission within our sample of galaxies using archival data from IRAC on the Spitzer Space Telescope (SST). The rest-frame 8 μm emission from galaxies has historically been used as a monochromatic SFR indicator. This is justified by the fact that polycyclic aromatic hydrocarbons (PAHs) and small dust grains can be heated by the photodissociation regions (PDRs) that surround actively star-forming regions and the subsequent emission from PAHs is brightest at about 8 μm (Draine & Li 2007; Smith et al. 2007; Kennicutt et al. 2009; Elbaz et al. 2011). However, there is an important caveat; PAHs are destroyed by ionizing radiation from newly formed stars (Helou et al. 2004; Povich et al. 2007; Bendo et al. 2008; Relaño & Kennicutt 2009), leading to a deficit in the 8 μm luminosity in galaxies with low metallicity, whereas PAHs are less well shielded by metals (Engelbracht et al. 2005; Calzetti et al. 2007; Smith et al. 2007; Cook et al. 2014; Shivaei et al. 2017). It has also been argued that metals can act as catalysts for the formation and growth of PAHs, leading to smaller average sizes in low-metallicity environments (Sandstrom et al.2012). More recently, Lin et al. (2020) find an anticorrelation between the dust-only 8 μm luminosity and the age of young stellar clusters, suggesting the 8 μm luminosity decreases with increasing age of the stellar population. The existence of a strong interstellar radiation field is also found to suppress the emission from PAHs, independent of metallicity (Madden et al. 2006; Gordon et al. 2008; Lebouteiller et al. 2011; Shivaei et al. 2017; Binder & Povich 2018). Adding to the complexity, spatially resolved studies have shown that a significant amount of 8 μm emission is associated with the cold, diffuse ISM, which suggests an important heating source other than recent (100 Myr) star formation (Bendo et al. 2008; Calapa et al. 2014; Lu et al. 2014). These results suggest that the luminosity at 8 μm is not a straightforward indicator of the SFR. In this work, we further investigate these issues by exploring how metallicity gradients within our sample affect the observed “red” side versus “blue” side IR color–color correlations—where we derive the “blue” side color from the flux density ratios at 8 and 24 μm.

2019AandA...631A...5Z__Mink_et_al._(2013)_Instance_1

Post-main-sequence mergers. Mergers of evolved stars with their MS companions originate from wider systems than main-sequence mergers (log P ≳ 0.7). We thus roughly estimate f P , i ∼ IPF 0.7 3.0 ∼ 0.66 $ f_{P , \mathrm{i}} \sim \mathrm{IPF}_{0.7}^{3.0} \sim 0.66 $ , using log10P = 3 as the maximum initial period for binary interaction (e.g., Claeys et al. 2011; Yoon et al. 2017). For merging to occur, a CEE phase needs to be initiated. Extreme mass ratio systems are prone to unstable mass transfer and thus to CEE, and although the exact value of the boundary is not well-constrained, we assume that this occurs for evolved donors in systems with q ≲ 0.4, following previous works such as Wellstein et al. (2001), Hurley et al. (2002) and de Mink et al. (2013). In principle, CEE can alternatively lead to the ejection of the envelope, but in this simple estimate we assume that all these systems eventually merge, leaving some hydrogen-rich layers on the surface of the formed star. This is consistent in most of the cases with the findings from our computational simulations for our standard assumption of using the entire orbital energy change to eject the envelope (αCEE = 1, as we will introduce and discuss in Sect. 4.1). Thus, f q , i ∼ IQF 0.1 0.4 ∼ 0.33 $ f_{q , \mathrm{i}} \sim \mathrm{IQF}_{0.1}^{0.4} \sim 0.33 $ . The projection of the initial parameter space for this channel at the log P − q plane is depicted in magenta on the left panel of Fig. 2. The donor star in such systems originate from a roughly similar part in M1 space as main-sequence mergers ( f M 1 ,i ~ IMF 7 25 ~1.25 $ f_{M_1 , {\rm i}} \sim {\rm IMF}_7^{25} \sim 1.25 $ ). In contrast, binaries of less extreme mass ratio are able to either follow a phase of stable mass transfer onto the secondary star or are assumed to always survive a CEE by ejecting the envelope, avoiding a merger. Such donors are expected to eventually produce a hydrogen-poor, stripped-envelope SN, not contributing to the SN II population that we focus on in this study. We find XpostMS + MS mergers ∼ C × 0.136 ∼ 15%.

2022AandA...661A..10B__Ghirardini_et_al._2021a_Instance_3

It is also possible that these clusters have a smaller extent and can just be missed by our extent selection as our detection algorithm sets the extent to zero if it is smaller than 6 (Brunner et al. 2022). Following the method presented in Ghirardini et al. (2021a), we estimated several dynamical properties of the clusters in the point source sample and compared them with the extent-selected sample presented in Ghirardini et al. (2021a). In Fig. 5 we compare the distributions of the core radii (Rcore) constrained by the V06 model and the concentration parameter (cSB) between these two samples. The concentration parameter is defined as the ratio of the surface brightness within 0.1 R500 to the surface brightness within R500 (Ghirardini et al. 2021a; Santos et al. 2008; Maughan et al. 2012). Intuitively, the expectation is that the smaller the core radius, the more compact the cluster. The left panel of Fig. 5 clearly shows that the clusters in the point source sample have relatively smaller core radii, hence the emission is more concentrated in a smaller area. Consistently, the concentration of the point source sample shows a clear excess in higher values than the extent-selected sample, indicating that a significantly larger fraction of cool-core clusters and clusters host a central AGN. We performed the same experiment by applying cuts in flux 1.5 × 10−14 ergs s−1 cm−2 and in detection likelihood to test whether the clusters are missed by the extent selection because they are fainter and/or more compact than the extent-selected clusters. The distribution of number density, core radius, and concentration parameter remains the same, indicating that the population of clusters in the point source catalog is more compact than the extent-selected sample. The extent-selected sample does not show a clear bias toward cool-core clusters or clusters with a central AGN, but contains the fraction of cool-cores is similar to that of SZ surveys (Ghirardini et al. 2021a). In this sample, we observe the opposite trend.

2018MNRAS.477.2220T__Miettinen_2014_Instance_1

We further restricted our sample to well-defined N2H+ (1–0) spectra that we used to estimate the gas velocity dispersion. The N2H+ (1–0) emission of each clump was evaluated from the MALT90 data cubes by averaging the spectrum across all the pixels within one MALT90 beam, ≃ 38 arcsec. We assumed that all the N2H+ emission comes from the clumps, and we estimate the filling factor from the comparison of the radius of each Hi-GAL clump with the radius of a region equal to the MALT90 beam (Fig. 1). There is a strong correlation between these two quantities, and the size of the Hi-GAL clumps is systematically smaller than the radius estimated from the MALT90 beam for a factor of 0.64, on average. We assumed an average filling factor of 0.64 for the entire sample. The MALT90 data cubes are given in antenna temperature $T_{\rm A}^{*}$ and they have been converted to the main beam temperature $T_{\rm MB}=T_{\rm A}^{*}/\eta _{\rm MB}$, assuming a mean beam efficiency ηMB = 0.49 (Miettinen 2014). The properties of each N2H+ (1–0) averaged spectrum have been extracted in idl using a hyperfine fitting routine and the mpfitfun algorithm (Markwardt 2009), after smoothing the data to a spectral resolution of 0.3 km s−1 to enhance the signal-to-noise (S/N) ratio. We excluded all clumps with a S/N ratio below 5, where the rms in each smoothed data cube has been measured in a 100 km s−1 wide spectral window near the N2H+ emission. We further excluded clumps for which the fit converged but the spectrum was affected by spikes and/or by multiple components along the line of sight. Using these criteria, we obtained 308 clumps. We completed our selection by excluding all clumps without a clear distance assignation, in particular without a well-defined resolution of the near–far distance ambiguity. First, we have refined the kinematic distances in the Elia et al. (2017) catalogue (and the quantities that depend on them) with the newest set of distances developed for the Hi-GAL survey under the VIALACTEA project (Mege et al., in preparation). The method used by Elia et al. (2017) was the same as in Russeil et al. (2011): the brightest emission lines in the 12CO or 13CO spectra along the line of sight of each source are used to estimate the velocities of the local standard of rest and converted into heliocentric distances using the Brand & Blitz (1993) rotation curve. The distances in Mege et al. (in preparation) have been determined using a similar approach, but including all the recent surveys of the Galactic plane to trace structures along the line of sight, and using the more recent Reid et al. (2009) rotation curve. Then, in order to identify only clumps with a well-defined distance estimation, we have compared the distances assigned to our 308 clumps with the distances of the MALT90 sample estimated in Whitaker et al. (2017) and of the ATLASGAL sources published in Urquhart et al. (2018). We excluded from the sample all sources with a difference in the distance estimation larger than 20 per cent among the three surveys.

2022ApJ...925..123N__Tielens_&_Charnley_1997_Instance_1

Benzene (C6H6), the simplest aromatic hydrocarbon, is a molecule that has raised great interest in the astrophysical community for almost four decades. This is mainly because C6H6 is one of the main precursors of polycyclic aromatic hydrocarbons (PAHs) reported to be present in interstellar dust particles (Leger & Puget 1984; Allamandola et al. 1989; Tielens 2013 and references therein), carbonaceous chondrites (Pering & Ponnamperuma 1971; Hayatsu et al. 1977; Hahn et al. 1988), and other astrophysical environments, such as carbon-rich, high-temperature environments (circumstellar and carbon-rich protoplanetary nebulae; Buss et al. 1993; Clemett et al. 1994). Benzene rings easily produce more complex, polycyclic structures by the one-ring build-up mechanism (Simoneit & Fetzer 1996). In space, an analogous process to carbon soot formation occurring on Earth can be initiated through the completion of that first aromatic ring and may also lead to the synthesis of PAHs (Tielens & Charnley 1997). Mechanisms involving the addition of hydrocarbons, such as acetylene onto aromatic rings as well as the attachment of other aromatic rings, or hydrocarbon pyrolysis, have been proposed to characterize the growth process of PAHs (Bittner & Howard 1981; Frenklach & Feigelson 1989; Wang & Frenklach 1997; Cherchneff 2011 and references therein). PAH synthesis from shocked benzene has also been reported (Mimura 1995). PAHs are crucial materials involved in a variety of cosmochemical processes. For example, amino acids could be synthesized by aqueous alteration of precursor PAHs in carbonaceous chondrites (Shock & Schulte 1990). PAHs are also produced in laboratory-simulated planetary atmospheres of Titan and Jupiter (Sagan et al. 1993; Khare et al. 2002; Trainer et al. 2004), and results from these studies indicate that the formation of aromatic rings and polyaromatics may be, among other sources, a possible chemical pathway for the production of the atmospheric solid particles (Lebonnois et al. 2002; Wilson et al. 2003; Trainer et al. 2004). The formation and evolution of benzene in planetary environments or other solar system objects thus represents a fundamental primary stage of the PAH production and other subsequent relevant chemical and prebiotic processes (like soot formation). In this context, several works related to benzene have been devoted to better understand the physico-chemical processes of irradiated C6H6, in its gaseous and solid phases, and the derived products, by acquiring high-resolution astronomical spectra, carrying out detailed laboratory studies or developing theoretical modeling (Allamandola et al. 1989 and references therein; Callahan et al. 2013; Materese et al. 2015; Mouzay et al. 2021). Laboratory astrophysical investigations have mostly focused on performing vibrational spectroscopy of ion, electron, or UV irradiated C6H6 gas and C6H6 ice. Such investigations aim to provide data on the spectral properties of the irradiated C6H6 materials, compare them with spectra obtained from astronomical observations (e.g., observations of the interstellar medium), or to study photoprocessed benzene ices to understand the fate of benzene ices in Titan’s stratosphere and help understanding the formation of aerosol analogs observed in Saturn’s moon’s stratosphere (Mouzay et al. 2021).

2022MNRAS.515.5135H__Zenteno-Quinteros,_Viñas_&_Moya_2021_Instance_1

In the present work, we neglect the processes that generate the halo, but this topic deserves some review. Notably, the apparent growth of the halo at the expense of the anti-sunward suprathermal ‘strahl’ population may imply that the halo is locally formed in the inner heliosphere by scattered strahl electrons (e.g. Maksimovic et al. 2005; Štverák et al. 2009). This has led to significant theoretical development, focused on the resonant interaction of electrons with the whistler and fast-magnetosonic whistler (FM/W) modes (e.g. Vocks et al. 2005; Saito & Gary 2007; Vasko et al. 2019; Verscharen et al. 2019b; Micera et al. 2021; Zenteno-Quinteros, Viñas & Moya 2021; Tang, Zank & Kolobov 2022; Vo, Lysak & Cattell 2022). Observations have struggled to confirm these theories. Notably, whistlers are practically absent (occurrence rate 0.1 per cent) during PSP perihelion passes (Cattell et al. 2022). Additionally, the eVDFs sampled by Helios and PSP are stable with respect to the oblique FM/W mode (Jeong et al. 2022a). Theoretical calculations show that at r ≲ 1 AU, the strahl is stable to whistler fluctuations (Horaites et al. 2018b; Schroeder et al. 2021) and should be unaffected by whistler turbulence in the inner heliosphere (Boldyrev & Horaites 2019). High-resolution measurements of the strahl at 1 AU confirm that ‘anomalous diffusion’, e.g. from whistler waves, is not required to explain the strahl angular widths at resolvable energies ≲300 eV Horaites et al. (2018a), Horaites, Boldyrev & Medvedev (2019). Similar results were found from simulations at distances r ≲ 20RS (Jeong et al. 2022b), which showed that near the corona the strahl is adequately described by a combination of Coulomb collisions and expansion effects. This all suggests that a mechanism besides local wave particle scattering may account for the halo’s presence in the inner heliosphere. Such theories have been proposed (e.g. Leubner 2004; Lichko et al. 2017; Che et al. 2019; Horaites et al. 2019; Scudder 2019), though no consensus has emerged.

2015ApJ...800...38G___2003_Instance_1

The currently accepted cold dark matter dominated model with the cosmological constant (ΛCDM) predicts that structures in our Universe assemble hierarchically, with more massive systems forming later through accretion and mergers of smaller, self-bound dark-matter halos (e.g., Tormen 1997; Moore et al. 1999; Klypin et al. 1999; Springel et al. 2001). In N-body cosmological simulations, dark matter halos of all masses converge to a roughly “universal” and cuspy density profile that steepens with radius, the so-called Navarro–Frenk–White profile (NFW profile; Navarro et al. 1996, 1997). Moreover, the degree of central concentration of a halo depends on its formation epoch and hence on its total mass (e.g., Wechsler et al. 2002; Zhao et al. 2003). Within this scenario, early virialized objects are compact when they get accreted into a larger halo. Such objects are usually referred to as subhalos or substructures of their host and, as they orbit within the host potential well, they are strongly affected by tidal forces and dynamical friction, causing mass, angular momentum, and energy loss (e.g., Ghigna et al. 1998; Tormen et al. 1998; De Lucia et al. 2004; Gao et al. 2004). In the ΛCDM framework, more massive halos are predicted to have a larger fraction of mass in subhalos than lower mass halos because in the former there has been less time for tidal destruction to take place (e.g., Gao et al. 2004; Contini et al. 2012). On galaxy cluster scales, observational tests of these predictions have been attempted in some previous works (e.g., Natarajan et al. 2007, 2009), but highly accurate analyses are becoming possible only now, thanks to the substantially improved quality of the available photometric and spectroscopic data. From an observational point of view, more investigations are still required to fully answer key questions on the formation and evolution of subhalos. How much mass of subhalos is stripped as they fall into the host potential? How many subhalos survive as bound objects? What are the spatial and mass distributions of the subhalos?

2015MNRAS.453.2126M__Hubrig_et_al._2013_Instance_1

There are additional lines of evidence suggesting that accretion in HD 100546 could be magnetospheric, and not through a BL. Equation 3 in Johns-Krull, Valenti & Koresko (1999) provides a lower limit to the magnetic field necessary to drive MA, in terms of the stellar parameters and accretion rate. Assuming a mass accretion rate of ∼10−7 M⊙ yr−1, the minimum stellar mass and maximum stellar radius allowed by the uncertainties provided by Fairlamb et al. (2015), and a minimum rotation period of 0.26 d (from a maximum projected rotational velocity vsin i = 65 km s−1 and a minimum inclination i = 22°, from Guimarães et al. 2006, and TAT11), HD 100546 could require a magnetic field of only several tens of Gauss to drive accretion magnetospherically.1 This is consistent with the magnetic field measured by Hubrig et al. (2009), 89 ± 26 G, although non detections have also been reported (Donati et al. 1997; Hubrig et al. 2013). In addition, if accretion in HD 100546 is actually magnetospheric the Keplerian gaseous disc should be truncated at a stelleocentric radius that increases with the stellar magnetic field and radius, and decreases with the accretion rate and stellar mass (Elsner & Lamb 1977). Using equation 6 in Tambovtseva et al. (2014), the disc truncation radius of HD 100546 should be ≲ 0.01 au (Fig. 3). This value is consistent with the Keplerian radius inferred from the width of the Br γ line profile (∼ ± 200 km s−1; Section 3); once this value is de-projected using the inclination in TAT11, the corresponding Keplerian distance is ≲ 0.02 au. It is noted that, in contrast to optically thick lines like H α, Br γ is mainly broadened by the Doppler effect (Tambovtseva et al. 2014). If the Keplerian disc is truncated by the stellar magnetic field, the gas would then fall ballistically on to the stellar surface. This could eventually be traced from the presence of redshifted absorptions at free-fall velocities in the profiles of several lines. In fact, previous spectroscopic analysis involving optical/UV lines suggest MA/ejection processes in HD 100546 (Vieira et al. 1999; Deleuil et al. 2004), with redshifted absorptions at velocities comparable to free-fall (Guimarães et al. 2006). However, those signatures are not observed in the Br γ profile of HD 100546. The unresolved component contributing ∼35 per cent to the visibility (Section 4.1) could be related to magnetospheric infall, but the small spatial scales involved cannot be probed from our observations.

2022AandA...664A...2T__Virtanen_et_al._2017_Instance_1

The earliest observations of magnetic fields in astrophysics were sunspot field strength measurements at the Mount Wilson Observatory (MWO) in California, USA, in 1908 (Hale 1908). Observations employed the Zeeman effect and were based on measuring the separation (splitting) between the two components of a spectral line, first Fe I 6173 Å and later Fe II 5250 Å. Daily observations of sunspot magnetic fields have been conducted since 1917 (Hale et al. 1919). In the early 1950s, the invention of an electronic magnetograph (Babcock 1953) allowed the measurement of regions with weaker magnetic fields than in sunspots, such as plages. Regular full-disk magnetograms have been observed since early 1960, first at MWO (Howard 1974) and then at the National Solar Observatory (NSO) at Kitt Peak starting in 1973 (Livingston et al. 1976). In our long-term project aimed at reconstructing the past magnetic activity on the Sun (Pevtsov et al. 2016, 2019; Virtanen et al. 2017, 2018, 2019a,b), we also use indirect measurements of magnetic fields obtained, in particular, from chromospheric spectroheliograms. Recently, Chatzistergos et al. (2021) and Shin et al. (2020) have used chromospheric observations to reconstruct the solar magnetic activity in the past. Reconstructing past magnetic fields is important for understanding the long-term behavior of the Sun since it is the main factor affecting space climate. Ca II K spectroheliograms are essential for this task, since the first ones were taken already in the 1890s in Europe (Paris and Meudon Observatories, France; Deslandres 1909; Malherbe & Dalmasse 2019) and in the United States (Kenwood Observatory; Hale 1893). Continuous observation campaigns in the Ca II K line started in the early 20th century in Kodaikanal (India), Mount Wilson Observatory (USA), the National Observatory of Japan (Japan), Paris-Meudon Observatory (France), the Arcetri Astrophysical Observatory (Italy), and the Astronomical Observatory of Coimbra University (Portugal) (for a historical overview see, e.g., Bertello et al. 2016; Chatzistergos et al. 2020a). Chromospheric spectroheliograms, together with flux transport simulations, have also been successfully used to study the evolution of large-scale solar magnetic fields, particularly the polar fields, which are only partially observable (Virtanen et al. 2019a).

2016ApJ...832..195N__Jin_et_al._2012_Instance_3

We ignore the density stratification effect in Case I, II, and IIa, because the width of the horizontal current sheet in our simulations is much shorter than the length. The simulation domain extends from x = 0 to x = L0 in the x-direction, and from y = − 0.5 L 0 to y = 0.5 L 0 in the y-direction, in the three cases, with L 0 = 10 6 m. Outflow boundary conditions are used in the x-direction and inflow boundary conditions in the y-direction. For the inflow boundary conditions, the fluid is allowed to flow into the domain but not to flow out; the gradient of the plasma density vanishes; the total energy is set such that the gradient in the thermal energy density vanishes; a vanishing gradient of parallel components plus divergence-free extrapolation of the magnetic field. For the outflow boundary conditions, the fluid is allowed to flow out of the domain but not to flow in, and the other variables are set by using the same method as the inflow boundary conditions. The horizontal force-free Harris current sheet is used as the initial equilibrium configuration of magnetic fields in Case I, 13 B x 0 = − b 0 tanh [ y / ( 0.05 L 0 ) ] 14 B y 0 = 0 15 B z 0 = b 0 / cosh [ y / ( 0.05 L 0 ) ] . The magnetic fields in the low solar atmosphere could be very strong (Jin et al. 2009, 2012; Khomenko et al. 2014; Peter et al. 2014; Vissers et al. 2015) and the magnetic field can exceed 0.15 T in both the intranetwork and the network quiet region (e.g., Orozco Suárez et al. 2007; Martínez González et al. 2008; Jin et al. 2009, 2012). In the work by Jin et al. 2012, the maximum of the field strength was found to be 0.15 T. The magnetic field could be even stronger in the active region near the sunspot. Therefore, we set b0 = 0.05 T in Case I and Case II, and b0 = 0.15 T in Case IIa. Due to the force-freeness and neglect of gravity, the initial equilibrium thermal pressure is uniform. The initial temperature and plasma density are set as T0 = 4200 K and ρ0 = 1.66057 × 10−6 kg m−3 in Case I, and T0 = 4800 K and ρ0 = 3.32114 × 10−5 kg m−3 in Case II and Case IIa. Therefore, the initial plasma β is calculated as β ≃ 0.0583 in Case I, β ≃ 1.332 in Case II, and β ≃ 0.148 in Case IIa. The initial ionization degree is assumed as Yi = 10−3 in Case I, and Yi = 1. 2 × 10−4 in Case II and IIa. The magnetic diffusion in this work matches the form computed from the solar atmosphere model in Khomenko & Collados (2012), and we set η = [ 5 × 10 4 ( 4200 / T ) 1.5 + 1.76 × 10 − 3 T 0.5 Y i − 1 ] m2 s−1 in Case I, and η = [ 5 × 10 4 ( 4800 / T ) 1.5 + 1.76 × 10 − 3 T 0.5 Y i − 1 ] m2 s−1 in Case II and IIa. The first part ∼ T−1.5 is contributed by collisions between ions and electrons, the second part ∼ T 0.5 Y i − 1 is contributed by collisions between electrons and neutral particles. Small perturbations for both magnetic fields and velocities at t = 0 make the current sheet to evolve and secondary instabilities start to appear later in the three cases. The forms of perturbations are listed below: 16 b x 1 = − pert · b 0 · sin 2 π y + 0.5 L 0 L 0 · cos 2 π x + 0.5 L 0 L 0 17 b y 1 = pert · b 0 · cos 2 π y + 0.5 L 0 L 0 · sin 2 π x + 0.5 L 0 L 0 18 v y 1 = − pert · v A 0 · sin π y L 0 · random n Max ( ∣ random n ∣ ) , where pert = 0.08, vA0 is the initial Alfvén velocity, randomn is the random noise function in our code, and Max ( ∣ random n ∣ ) is the maximum of the absolute value of the random noise function. This random noise function makes the initial perturbations for the velocity in the y-direction to be asymmetric, and such an asymmetry makes the current sheet gradually become more tilted, especially after secondary islands appear. The reconnection process is not really symmetrical in nature (Murphy et al. 2012), this is one of the reasons that we use such a noise function. Another reason is that the asymmetric noise function makes the secondary instabilities develop faster. Figure 1(a) shows the distributions of the current density and magnetic fields at t = 0 in case I.

2020AandA...635A..47H__Heckman_et_al._2000_Instance_1

Finally, the multiphase nature of galactic outflows implies that measurements of the outflow properties based on a single gas phase can lead to misleading conclusions (for a discussion, see e.g., Cicone et al. 2018b). Historically, systematic studies of galactic outflows in nearby and high-z galaxies have focused on the ionized gas – for example, as observed as broad wing emission in the spectra of the Hα, [O III] or Paα lines – (e.g., Heckman et al. 1990; Rupke & Veilleux 2013a; Woo et al. 2016; Harrison et al. 2016; Förster Schreiber et al. 2019; Ramos Almeida et al. 2019) and the atomic phase – based on the Na D or Mg II lines in absorption (e.g., Heckman et al. 2000; Rupke et al. 2002, 2005; Weiner et al. 2009; Roberts-Borsani & Saintonge 2019). The molecular component of outflows, on the other hand, has been much more difficult to study. Great progress was made with the Herschel Space Observatory using the OH 119 μm line in absorption to study molecular outflows in Seyfert and luminous infrared galaxies (Fischer et al. 2010; Sturm et al. 2011; Veilleux et al. 2013; Bolatto et al. 2013; Spoon et al. 2013; George et al. 2014; Stone et al. 2016; González-Alfonso et al. 2017; Zhang et al. 2018). More recently, the advent of powerful millimeter-wave interferometers such as the Atacama Large Millimeter/submillimeter Array (ALMA) and the NOrthern Extended Millimeter Array (NOEMA) are rapidly increasing the number of molecular outflows detected based on observations of the CO line (e.g., Combes et al. 2013; Sakamoto et al. 2014; García-Burillo et al. 2014; Leroy et al. 2015; Feruglio et al. 2015; Morganti et al. 2015; Dasyra et al. 2016; Pereira-Santaella et al. 2016, 2018; Veilleux et al. 2017; Fluetsch et al. 2019; Lutz et al. 2020). At high-z, so far only a handful of large-scale, molecular outflows have been studied in QSOs (e.g., Cicone et al. 2015; Vayner et al. 2017; Feruglio et al. 2017; Carniani et al. 2017; Fan et al. 2018; Brusa et al. 2018), sub-millimeter galaxies (e.g., Spilker et al. 2018), and main-sequence, star-forming galaxies (e.g., Herrera-Camus et al. 2019).

2022ApJ...940...72R__Camilo_et_al._2006_Instance_2

Several studies have discussed the radio luminosity of GLEAM-X J1627 during its radio outburst in comparison with the limits of its rotational energy (Erkut 2022; Hurley-Walker et al. 2022). In particular, assuming isotropic emission, the radio luminosity of the brightest single peaks (L radio ∼ 1030–1031 erg s−1; Hurley-Walker et al. 2022) exceeds the limits on the rotational power of the source by a few orders of magnitude. Figure 6 shows those peak radio luminosities and the rotational power of GLEAM-X J1627 in comparison with other pulsars, rotating radio transients (RRATs) and radio-loud magnetars. For the radio-loud magnetars, given their large variability, we have chosen the brightest radio pulses reported in the literature (data collected from Camilo et al. 2006, 2007; Weltevrede et al. 2011; Deller et al. 2012; Lynch et al. 2015; Majid et al. 2017; Pearlman et al. 2018; Lower et al. 2020, and Esposito et al. 2021). It is well known that assuming isotropic radio emission is not realistic, and a beaming factor necessarily has to be present (see, e.g., Erkut 2022). However, the relation between the duty cycle and the spin period of canonical pulsars has a large spread (Manchester et al. 2005). Moreover, it is observed that this relationship does not apply to radio-loud magnetars, which in general show larger duty cycles than what one would expect from the extrapolation of this tentative relation for radio pulsars to magnetars (see, e.g., Camilo et al. 2006, 2007). To avoid the uncertainty of beaming models, which for magnetars are mostly unknown even theoretically, we plotted the isotropic radio luminosity for all the different pulsar classes in Figure 6. From this plot, at variance with canonical radio pulsars, we see how the brightest single peaks for radio-loud magnetars might exceed their rotational powers, in line with what is possibly observed for GLEAM-X J1627. While not resolving uncertainties related to the exact mechanism of radio emission or the beaming factor, Figure 6 shows that, under the assumption of isotropic emission, even for magnetars the brightest single peaks exceed their rotational energy budget. Considering all the uncertainties in the assumptions used to derive the radio luminosities plotted in Figure 6, GLEAM-X J1627’s radio luminosity excess over its rotational power cannot be used as an argument for or against its neutron star nature.

2019AandA...631A..88Y__Bohren_&_Huffman_(1998)_Instance_1

Starting from the four aforementioned materials, we consider several composition mixtures and grain structures. For the sake of comparison, we first consider compact grains of purely a-Sil, a-C, or a-C:H. Subsequently, according to Köhler et al. (2015), we consider compact grains made of two thirds a-Sil and one third a-C (Mix 1) or one third a-C:H (Mix 2), in terms of volume fractions. These allow reproduction of the mass fractions derived by Jones et al. (2013) for the diffuse ISM. The effect of porosity is tested for the Mix 1 mixture, with a porosity degree of 50% (Mix 1:50). We also evaluate theimpact of the presence of a water ice mantle on compact Mix 1 grains (Mix 1:ice). We further consider two material compositions defined in Pollack et al. (1994) based on depletion measurements: (i) 21% a-Sil and 79% a-C (Mix 3); and (ii) 8% a-Sil, 30% a-C, and 62% water ice (Mix 3:ice). The various grain compositions are summarised in Table 1. For each grain composition, we derive the absorption and scattering efficiencies Qabs and Qsca, respectively, and the asymmetry factor of the phase function g = ⟨cosθ⟩. To allow fast calculations, we make the major assumption that the grains are spherical and compute their optical properties using the Mie theory (Mie 1908; Bohren & Huffman 1983) with the Fortran 90 version of the BHMIE routine given in Bohren & Huffman (1998). For grains consisting of two or three materials, we first derive effective optical constants following the Maxwell Garnett mixing rule (Maxwell Garnett 1904; Bohren & Huffman 1998). Indeed, we assume that in Mix 1 grains, for example, carbon appears as proper inclusions in the silicate matrix rather than assuming a completely random inhomogeneous medium. Mishchenko et al. (2016a,b) performed exhaustive studies of the applicability of the Maxwell Garnett mixing rule to heterogeneous particles. These latter authors showed that this rule can provide accurate estimates of the scattering matrix and absorption cross-section of heterogeneous grains at short wavelengths (typically up to the visible for a 0.1 μm grain and to the mid-infrared(MIR) for a 10 μm grain) if twocriteria are met: both the size parameter of the inclusions and the refractive index contrast between the host material and the inclusions have to be small. Moreover, Mishchenko et al. (2016a) demonstrated that the extinction and asymmetry-parameter errors of the Maxwell Garnett mixing rule are significantly smaller than the scattering-matrix errors, remaining small enough for most typical applications and in particular the kind of applications we perform here. It is however well known that this kind of mixing rule systematically underestimates the absorption efficiency in the FIR to millimetre wavelength range, the implications of which are discussed in Sect. 3.2. We perform our computations with the emc routine of V. Ossenkopf3. For Mix 1 and Mix 2, we assume a matrix of a-Sil with inclusions of a-C or a-C:H, and for Mix 3 a matrix of a-C with inclusions of a-Sil. For grains surrounded by an ice mantle, the optical properties are derived with the core-mantle Mie theory using the BHCOAT routine given in Bohren & Huffman (1998).

2022AandA...667A..35R__Nakajima_&_Ouchi_(2014)_Instance_1

In the optical range, several proxies can serve as indicators of LyC leakage. In particular, line ratios involving ions with different ionization potentials, produced at different depths, have been proposed as indicators to discriminate between radiation-bounded and density-bounded H II regions. Radiation-bounded regions correspond to ionized spheres delimited by their Strömgren radii set by the equilibrium between production of photons by stars and ionization of the surrounding gas. Density-bounded regions are instead delimited by the lack of matter, which sets their outer radius before the Strömgren radius. Hence, they allow part of the LyC-photons produced by stars to escape from H II regions. The oxygen line ratio [O III]λ 5007 Å/[O II] λλ3726, 3728 Å (O32) proposed by Jaskot & Oey (2013) and Nakajima & Ouchi (2014) was successfully used to select LyC-leaking candidates but no strong correlation was found with the measured values of escape fraction (see Izotov et al. 2018b; Naidu et al. 2018; Bassett et al. 2019; Nakajima et al. 2020, and discussions therein). Based on a similar idea, the lack of emission from ions with low ionization potentials like [S II] λλ6716,6731 Å has also been proposed to target leaking candidates (Wang et al. 2019, 2021; Katz et al. 2020). This lack of emission of some low ionization species was first interpreted as the signature of a density-bounded galaxy where the outer part of H II regions were completely stripped out. However, using simple photoionization models, Stasińska et al. (2015) have shown that on average galaxies with high O32 cannot have massive escapes of ionizing photons, since low ionization lines like [O I]6300 Å are often also detected in these galaxies, implying the presence of radiation-bounded regions. Subsequently Plat et al. (2019) and Ramambason et al. (2020) noted that several strong LyC-emitters show surprisingly strong [O I]6300 Å emission, and proposed several explanations. While Stasińska et al. (2015) and Plat et al. (2019) suggested that such emission could be powered by the presence of AGN or radiative-shocks, we proposed in Ramambason et al. (2020) a 2-component model combining both density- and ionization-bounded regions.

2020MNRAS.492.4975M__Iben_1967_Instance_1

We emphasize that 7Be/H remains larger by at least one order of magnitude than predicted by nova models (Starrfield et al. 1978; Hernanz et al. 1996; José & Hernanz 1998). We note, however, that the final amount of 7Be is sensitive to the amount of 3He in the donor star as a higher abundance of 3He is expected to result in a higher 7Be abundance. Boffin et al. (1993) and Hernanz et al. (1996) found a logarithmic dependence of the 7Be output to the initial 3He abundance. The non-linearity of 7Be yields results from 3He(3He, 2p)4He and its importance increases as the square of the initial 3He abundance. Therefore, it produces a leaking of the available 3He for the 3He(α, γ) 7Be, whose rate increases only linearly for the initial 3He abundance. For 3He enhancements up to 100 solar, Boffin et al. (1993) derive X(7Be)/X(7Be0) = 1 + 1.5log X(3He)/X(3He⊙), where 7Be0 is the 7Be final mass fraction obtained with a solar initial 3He mass fraction. From the theoretical point of view, it is believed that low-mass main-sequence stars synthesize 3He through the p–p chains with peak abundances of few 10−3 by number (Iben 1967). As the star ascends, the red giant branch convection dredges up 3He-enriched material to the surface, which is later expelled into the interstellar medium by wind or during the planetary phase. 3He is a particularly difficult element to measure. It can be measured in H ii regions by using measurements at a frequency of 8.665 GHz (i.e. 3.46 cm), which is emitted naturally by ionized 3He (Bania, Rood & Balser 2010; Balser & Bania 2018) or in the stellar atmospheres of hot stars (Geier et al. 2012). Surprisingly, interstellar medium observations indicate that there is far less of this element in the Galaxy than the current models predict. In order not to overproduce 3He in the course of chemical evolution, it has become customary to assume that some unknown 3He-destruction mechanism is at work in low-mass giants (Dearborn, Steigman & Tosi 1996; Galli et al. 1997; Chiappini, Renda & Matteucci 2002; Romano & Matteucci 2003). For instance, Charbonnel & Zahn (2007) suggested a thermohaline mixing during the red giant branch phase of low-mass stars. However, in a few planetary nebulae, 3He/H is found to be high at the level of 10−3, consistent with predictions from standard stellar models (Rood, Bania & Wilson 1992; Balser & Bania 2018).

2022AandA...663A.110M__Tschudi_&_Schmid_2021_Instance_1

The presented model calculations provide two-dimensional images for the scattered intensity I(x, y), azimuthal polarization Qφ(x, y), and other radiation parameters. It is useful for the comparison with observations to deduce disk-integrated radiation parameters from these model images that are scaled to the stellar intensity, as in ${{\bar I\left( i \right)} \mathord{\left/ {\vphantom {{\bar I\left( i \right)} {{I_ \star }}}} \right. \kern-\nulldelimiterspace} {{I_ \star }}}{{,{{\bar Q}_\varphi }\left( i \right)} \mathord{\left/ {\vphantom {{,{{\bar Q}_\varphi }\left( i \right)} {{I_ \star }}}} \right. \kern-\nulldelimiterspace} {{I_ \star }}}$I¯(i)/I⋆,Q¯φ(i)/I⋆, the disk-averaged fractional polarization 〈pφ〉, or quadrant polarization values Qxxx(i)/I⋆ or Uxxx(i)/I⋆ which can also be deduced from observations without introducing significant ambiguities by the diversity of disk morphologies. We note that the measured values must be corrected for instrumental effects, in particular for the signal convolution with the instrumental PSF, which can introduce significant cancellation of the disk signal (Schmid et al. 2006; Avenhaus et al. 2014, Avenhaus et al. 2017; Heikamp & Keller 2019; Tschudi & Schmid 2021). Unfortunately, available observational data with high accuracy and quantified uncertainties are still very limited and often only some of the radiation parameters can be determined for a given disk. Therefore, the derivation of disk parameters from the comparison with model results can be rather ambiguous. The model results presented in this work allow us to carry out a detailed investigation of the important parameter ambiguities involved in the interpretation of observations of transition disks and provide diagnostic relations that can be used to constrain key model parameters for different types of observational data. For example, Fig. B.1 shows that, for a given inclination $i,{{\bar I} \mathord{\left/ {\vphantom {{\bar I} {{I_ \star }}}} \right. \kern-\nulldelimiterspace} {{I_ \star }}}{{,{{\bar Q}_\varphi }} \mathord{\left/ {\vphantom {{,{{\bar Q}_\varphi }} {{I_ \star }}}} \right. \kern-\nulldelimiterspace} {{I_ \star }}}$i,I¯/I⋆,Q¯φ/I⋆, and ${{\bar Q} \mathord{\left/ {\vphantom {{\bar Q} {{I_ \star }}}} \right. \kern-\nulldelimiterspace} {{I_ \star }}}$Q¯/I⋆ all strongly correlate with the angular wall height α and single scattering albedo ω , and anti-correlate with wall slope χ and scattering asymmetry parameter ɡ. In addition, the polarization parameters ${{{{\bar Q}_\varphi }} \mathord{\left/ {\vphantom {{{{\bar Q}_\varphi }} {{I_ \star }}}} \right. \kern-\nulldelimiterspace} {{I_ \star }}}$Q¯φ/I⋆ and ${{\bar Q} \mathord{\left/ {\vphantom {{\bar Q} {{I_ \star }}}} \right. \kern-\nulldelimiterspace} {{I_ \star }}}$Q¯/I⋆ also correlated with pmax. If only the scattered intensity ${{\bar I} \mathord{\left/ {\vphantom {{\bar I} {{I_ \star }}}} \right. \kern-\nulldelimiterspace} {{I_ \star }}}$I¯/I⋆ or only the polarized intensity is measured then it is almost impossible to constrain individual disk parameters without additional information. Therefore, it is important to select and obtain more observational information and better diagnostic parameters with more diagnostic power.

2015MNRAS.453.3414A__Filippenko_&_Chornock_2001_Instance_1

Filippenko & Chornock (2001) first presented the dynamical estimate of mass of the source to be around 7.4 ± 1.1 M⊙. Recently, Radhika & Nandi (2014) claimed that the mass of XTE J1859+226 is perhaps in between 6.58 and 8.84 M⊙ which is similar to the prediction of Shaposhnikov & Titarchuk (2009), although the lower mass limit is estimated as 5.4 M⊙ by Corral-Santana et al. (2011). However, we consider the typical mass of the source as 7 M⊙. The distance of this source is around d ∼ 11 kpc (Filippenko & Chornock 2001). Steiner et al. (2013) measured the spin as ak ∼ 0.4; however, Motta et al. (2014b) recently reported that the spin of the source is ak ∼ 0.34. Since the spin predictions are quite close, we use ak ∼ 0.4 for this analysis. We estimate the fluxes Fx (see Table 1) of LHS and HIMS of the 1999 outburst of the source (Radhika & Nandi 2014). The corresponding disc luminosities are calculated as $L_{\rm disc}^{{\rm LHS}}=8.26 \times 10^{37}\ {\rm erg\ s^{-1}}$ and $L_{\rm disc}^{{\rm HIMS}}=1.85 \times 10^{38}\ {\rm erg\ s^{-1}}$, respectively. Now, it is reasonable to assume the accretion efficiency for rotating BH as η = 0.3 which corresponds to the accretion rate of the inflowing matter as ${\dot{M}}_{{\rm acc}}^{{\rm LHS}} = 0.304 {\dot{M}}_{{\rm Edd}}$ in LHS and ${\dot{M}}_{{\rm acc}}^{{\rm HIMS}} = 0.680 {\dot{M}}_{{\rm Edd}}$ in HIMS. For LHS, we use $R_{\dot{m}}=9.83$ per cent following our theoretical estimate where xs = 64.6rg for ak = 0.4, ${\mathcal {E}}=0.001\,98$ and λ = 3.18. Incorporating these inputs in equation (15), we obtain the jet kinetic power as $L^{{\rm LHS}}_{{\rm jet}} = 2.52\times 10^{37}\ {\rm erg\ s^{-1}}$. The maximum mass outflow rate for HIMS corresponding to ak = 0.4 is obtained from Fig. 9 as $R^{\rm max}_{\dot{m}}=17.5$ per cent for ${\mathcal {E}}=0.005\,47$ and λ = 3.1, where the shock transition occurs at 21.9rg. Using these values in equation (15), we obtain the maximum jet kinetic power as $L^{{\rm HIMS}}_{{\rm jet}} = 1.08\times 10^{38}\ {\rm erg\ s^{-1}}$ which we regard to be associated with the HIMS of this source.

2022MNRAS.514.5570N__in_1928_Instance_1

In this study, we examine the stellar populations of NGC 5053 using the UVIT, on-board the AstroSat. NGC 5053 is a galactic GC that lies in the northern constellation of Coma Berenices, having Galactic coordinates l = 335${_{.}^{\circ}}$70, b = 78${_{.}^{\circ}}$95. This cluster is located at a distance of 17.54 ± 0.23 kpc (Baumgardt & Vasiliev 2021) and the metallicity is estimated to be [Fe/H] = −2.27 (Harris 2010). It is, therefore, placed amongst the metal-poor galactic GCs. The tidal radius of this cluster has been estimated as rt = 15.2 ± 3.3 arcmin by de Boer et al. (2019). When it was discovered in 1784, it was not classified as a GC because of its appearance and structure; there was no densely packed nucleus, and the central region was resolvable (Herschel 1786). These observations placed this GC in a grey-area between globular and open clusters. It was only in 1928 that Baade first classified it as a GC owing to its high latitude, richness in faint stars and presence of variable stars (Baade 1928). The variable stars in this cluster include RR Lyrae variables and SX Phoenicis (SX Phe) stars (Sawyer 1946; Nemec, Mateo & Schombert 1995a; Nemec et al. 1995b; Nemec 2004). The cluster is known to have a HB that is extended predominantly towards the blue flank of the RR Lyrae instability strip, which is an archetypal feature in many metal-poor GCs (Sarajedini & Milone 1995). A rich population of BSSs has been identified in this cluster (Sarajedini & Milone 1995). The presence of relatively large number of BSSs in such low density GCs has been examined to propose alternate formation mechanisms of BSSs (Leonard & Fahlman 1991). The cluster has been examined in the UV regime by Schiavon et al. (2012) using photometric data from GALEX, and they have published a catalogue of the UV bright stars present in this cluster. The location of this cluster in the sky and its velocity have given rise to a debate on whether it belongs to the Milky Way or Sgr dSph (e.g. Law & Majewski 2010; Boberg, Friel & Vesperini 2015; Sbordone et al. 2015; Tang et al. 2018).

2021MNRAS.506.1258W__Borsa_et_al._2021_Instance_1

Situated in the closest vicinity of their host stars (0.05 AU) and having no counterparts in our Solar system, ultra-hot Jupiters (Arcangeli et al. 2018; Bell & Cowan 2018; Parmentier et al. 2018) are ideal testbeds for studying the impact of 3D effects on high-resolution spectra. There are two important reasons for this. First, ultra-hot Jupiters are accessible objects to observe. Their short orbital periods (1–2 d) and hot, extended atmospheres make them perfect targets for transmission spectroscopy (Hoeijmakers et al. 2019; Von Essen et al. 2019; Ehrenreich et al. 2020; Borsa et al. 2021), emission spectroscopy (Evans et al. 2017; Arcangeli et al. 2018; Mikal-Evans et al. 2020) and phase-curve studies (Zhang et al. 2018; Bourrier et al. 2020b; Mansfield et al. 2020). Secondly, ultra-hot Jupiters display extreme variations across their atmospheres, because they are expected to become tidally locked soon after their formation (Rasio et al. 1996; Showman & Guillot 2002). As a result, their atmospheres virtually consist two different worlds: a permanently irradiated dayside and a permanently dark nightside. The scorching, cloud-free dayside (T ≳ 2500 K) nearly resembles a stellar photosphere, where most molecules are dissociated1 and metals become ionized (Parmentier et al. 2018; Hoeijmakers et al. 2019). On the other hand, the nightside is substantially cooler (T ≲ 1000 K) and may even serve as a stage for cloud formation (Helling et al. 2019; Ehrenreich et al. 2020). Ultra-hot Jupiters also exhibit large differences in their thermal structures: the dayside is expected to show strong thermal inversions (Haynes et al. 2015; Evans et al. 2017; Kreidberg et al. 2018; Pino et al. 2020; Yan et al. 2020), whereas nightside temperatures are expected to monotonically decrease with altitude. Furthermore, ultra-hot Jupiters feature strong winds in the order of 1–10 km s−1 (Tan & Komacek 2019), which arise as a result of the continuous day-night forcing. Many observational studies have measured Doppler shifts due to winds on ultra-hot Jupiters (Casasayas-Barris et al. 2019; Bourrier et al. 2020a; Cabot et al. 2020; Ehrenreich et al. 2020; Gibson et al. 2020; Hoeijmakers et al. 2020; Nugroho et al. 2020; Stangret et al. 2020; Borsa et al. 2021; Kesseli & Snellen 2021; Rainer et al. 2021; Tabernero et al. 2021), yet inferring the underlying 3D circulation pattern is a formidable challenge.

2019MNRAS.482.5651M__Schweizer_&_Middleditch_1980_Instance_2

Therefore, the kinetics characteristics of the star could be the only piece to judge whether or not the SM star is the surviving companion of SN 1006. If its space velocity is significantly different from the other stars in the remnant of SN 1006, the probability to be the surviving companion would become high. Otherwise, the probability becomes low. We check the proper motion of the stars within 5 arcmin of the remnant centre from Gaia DR2, as shown in Fig. 19. From the figure, it seems that there is not difference between the SM star and other stars in the remnant of SN 1006 in the aspect of proper motion, i.e. the proper motion of the SM star only slightly deviates from the median value of the proper motions of the stars at the direction of the SNR centre of SN 1006, and such a proper motion disfavours the SM star as the surviving companion of SN 1006 (Schweizer & Middleditch 1980; Burleigh et al. 2000). So, a 3D space velocity is helpful to judge the nature of the SM star. However, unfortunately, some data of the SM star in Gaia DR2 are so uncertain that we cannot use them to constrain its 3D space velocity, otherwise we could obtain a complete wrong conclusion.5 For example, the parallax of the SM star is ϖ = 0.0736 ± 0.1244, and then σϖ/ϖ = 1.69 which is much larger than the threshold value of 0.2 for distance estimation from GAIA DR2 data (Astraatmadja & Bailer-Jones 2016; Katz et al. 2018). The distance of the SM star from this parallax is much larger than all the previous measurements from spectrum by at least a factor of 2 (see summary in Burleigh et al. 2000). Considering that some other astrometric measurements of the SM star are also very uncertain, we applied the measurements in the previous literatures as the distance of the SM star. Based on a radial velocity of $-13\pm 17\, {\rm km^{\rm -1}}$ and a distance of 2.07 ± 0.18 kpc (Schweizer & Middleditch 1980; Winkler et al. 2003; Kerzendorf et al. 2018), we can calculate the UVW velocities of the SM star, i. e. $U=-5.2\pm 14\, {\rm km^{\rm -1}}$, $V=197\pm 10\, {\rm km^{\rm -1}}$, and $W=3.1\pm 5\, {\rm km^{\rm -1}}$. The V value of the SM star is smaller than that of a normal disc star. We then transform these velocities into the Galactic rotational velocity at a Galactocentric distance of ∼6.67 kpc, i.e. $V_{\rm c}=196\pm 12\, {\rm km^{\rm -1}}$, which is smaller than the Galactic rotational velocity of the disc stars at the Galactocentric distance by $50\pm 19\, {\rm km^{\rm -1}}$ (Huang et al. 2016). This velocity difference is marginally consistent with the predicted orbital velocity here (see Fig. 7). In addition, the smaller rotational velocity of the SM star may explain its small proper motion shown in Fig. 19. So, the SM star is still possible to be the surviving companion of SN 1006.

2016MNRAS.463..382U__Zdziarski_et_al._2000_Instance_1

Active galactic nuclei (AGN) are thought to be powered by an accretion disc around a supermassive black hole, mostly emitting in the optical/ultraviolet (UV) band. According to the standard paradigm, the X-ray emission is due to thermal Comptonization of the soft disc photons in a hot region, the so-called corona (Haardt & Maraschi 1991; Haardt, Maraschi & Ghisellini 1994, 1997). This process explains the power-law shape of the observed X-ray spectrum of AGN. A feature of thermal Comptonization is a high-energy cut-off, which has been observed around ∼100 keV in several sources thanks to past observations with Compton Gamma-Ray Observatory (CGRO)/Oriented Scintillation Spectrometer Experiment (OSSE; Zdziarski, Poutanen & Johnson 2000), BeppoSAX (Perola et al. 2002) and more recently Swift/Burst Alert Telescope (BAT; Baumgartner et al. 2013) and INTEGRAL (Malizia et al. 2014; Lubiński et al. 2016). From the application of Comptonization models, such high-energy data allow to constrain the plasma temperature, which is commonly found to range from 50 to 100 keV (e.g. Zdziarski et al. 2000; Lubiński et al. 2016). Furthermore, the cut-off energy is now well constrained in an increasing number of sources thanks to the unprecedented sensitivity of NuSTAR up to ∼80 keV (e.g. Ballantyne et al. 2014; Brenneman et al. 2014; Marinucci et al. 2014; Baloković et al. 2015; Matt et al. 2015; Ursini et al. 2015). The primary X-ray emission can be modified by different processes, such as absorption from neutral or ionized gas (the so-called warm absorber), and Compton reflection from the disc (e.g. George & Fabian 1991; Matt, Perola & Piro 1991) or from more distant material, like the molecular torus at pc scales (e.g. Matt, Guainazzi & Maiolino 2003). A smooth rise below 1–2 keV above the extrapolated high-energy power law is commonly observed in the spectra of AGN (see e.g. Bianchi et al. 2009). The origin of this so-called soft excess is uncertain (see e.g. Done et al. 2012). Ionized reflection is able to explain the soft excess in some sources (e.g. Crummy et al. 2006; Ponti et al. 2006; Walton et al. 2013), while Comptonization in a ‘warm’ region is favoured in other cases (e.g. Mehdipour et al. 2011; Boissay et al. 2014).

2021AandA...650A.205V__Hippke_&_Heller_2019_Instance_1

To search for transit events, we will make use of our custom pipeline SHERLOCK (Pozuelos et al. 2020)5. This pipeline provides the user with easy access to Kepler, K2, and TESS data for both SC and LC. The pipeline searches for and downloads the pre-search data conditioning simple aperture (PDC-SAP) flux data from the NASA Mikulski Archive for Space Telescope (MAST). Then, it uses a multi-detrend approach in the WOTAN package (Hippke et al. 2019), whereby the nominal PDC-SAP flux light curve is detrended several times using a biweight filter or a Gaussian process, by varying the window size or the kernel size, respectively. This multi-detrend approach is motivated by the associated risk of removing transit signals, in particular, short and shallow signals. Each of the new detrended light curves, jointly with the nominal PDC-SAP flux, is then processed through the transit least squares (TLS) package (Hippke & Heller 2019) in the search for transits. In contrast to the classical box least-squares (BLS) algorithm (Kovács et al. 2002), the TLS algorithm uses an analytical transit model that takes the stellar parameters into account. Then, it phase folds the light curves over a range of trial periods (P), transit epochs (T0), and transit durations (d). It then computes the χ2 between the model and the observed values, searching for the minimum χ2 value in the 3D parameter space (P, T0, and d). The TLS algorithm has been found to be more reliable than the classical BLS in finding any type of transiting planet, and it is particularly well suited for the detection of small planets in long time series, such as those coming from Kepler, K2, and TESS. The TLS algorithm also allows the user to easily fine-tune the parameters to optimize the search in each case, which is particularly interesting for shallow transits. In addition, SHERLOCK incorporates a vetting module that combines the TPFplotter (Aller et al. 2020), LATTE (Eisner et al. 2020), and TRICERATOPS (Giacalone et al. 2021) packages, which allows the user to explore any contamination source in the photometric aperture used, momentum dumps, background flux variations, x–y centroid positions, aperture size dependences, flux in-and-out transits, each individual pixel of the target pixel file, and to estimate the probabilities for different astrophysical scenarios such as transiting planet, eclipsing binary, and eclipsing binary with twice the orbital period. Collectively, these analyses help the user estimate the reliability of a given detection.

2021AandA...647A..49S__Pérez_et_al._2019_Instance_1

It has been claimed that, for solar-type stars, CA models could face some difficulties (in principle) explaining the presence of giant planets around metal-poor stars, or massive planets at long radial distances (see e.g., Helled et al. 2014). In particular, HR 8799 is a metal-poor A5 star included in our sample, hosting three giant planets orbiting beyond 10 AU (Marois et al. 2008). Then, some authors proposed that the planets orbiting HR 8799 are likely formed by GI, given that GI models can take place at large radii and in low metal environments (Marois et al. 2008; Dodson-Robinson et al. 2009; Meru & Bate 2010). Other works cast some doubts about the planet formation around HR 8799, showing that CA models are also possible (Currie et al. 2011) or even a combination of GI and CA (Marois et al. 2010). For the case of β Pictoris and HD 169142, also having giant planets at long distances, CA models seem to be favored (GRAVITY Collaboration 2020; Nowak et al. 2020; Pérez et al. 2019). However, an important word of caution about these works is in order. Some of the stars mentioned (HR 8799 and HD 169142) display (superficial) metal-poor abundances, showing in fact a λ Boötis pattern (see for example Fig. 8). As previously mentioned, the most accepted idea about the origin of this peculiar signature, suppose a solar-like composition for the original molecular cloud where the stars born, and then some kind of selective accretion to obtain a λ Boötis pattern. In this way, it would be not entirely appropriate to assume a metal-poor natal environment for stars like HR 8799, as assumed by some works to support a GI planet formation (e.g., Meru & Bate 2010). This fact was early noted by Paunzen et al. (2014), in their comparison of λ Boötis stars and Population II type stars. Numerical simulations of planet formation around λ Boötis stars should assume a solar-like composition (rather than a metal-poor natal environment), and this could have important consequences for the subsequent results.

2018MNRAS.473.4130M__Martin_&_Drissen_2016_Instance_1

The flux calibration was performed from two calibration sources: (1) the spectrum of the spectrophotometric standard star GD71 (Bohlin 2003), obtained in 2016 January, which is used to eliminate as much as possible any strong wavelength dependence; (2) the median combination of a set of 10 images of the standard star HZ 4 (Bohlin, Dickinson & Calzetti 2001) obtained right after the end of the cube observation with photometric conditions similar to the observation conditions. The exact value of the interferometer’s modulation efficiency (ME), which acts essentially as an additional throughput loss, is the major source of uncertainty on the absolute flux calibration (Martin & Drissen 2016). Interferometric images of the laser source have been obtained before and after the observation of the target in order to measure the variation of ME at the calibration laser wavelength (543.5 nm) with respect to its nominal value (85 per cent). We have measured a loss of 11.7 per cent. The initial flux calibration of M 31 in the SN3 filter has been corrected for this loss (thus multiplied by a factor 1.13). But given the possible uncertainty on this estimate, we have double-checked it using Hubble Space Telescope (HST) narrow-band images of the target. The advantage of HST’s narrow-band filters is that they can easily be simulated by integrating the spectral cube over the filter’s well-known transmission curve. As SITELLE’s cube flux is expressed in erg cm−2 s−1 Å−1 and given the filter transmission curve F(σ), the integrated flux $\tilde{\phi }_{\rm F}$, expressed in erg cm−2 s−1 Å−1, is (15) \begin{equation} \tilde{\phi }_{\rm F} = \frac{\int _{-\infty }^{+\infty }\phi (\sigma )F(\sigma ){\rm d}\sigma }{\int _{-\infty }^{+\infty }F(\sigma ){\rm d}\sigma }\, . \end{equation} If one converts the flux in terms of surface brightness, expressed in erg cm−2 s−1 Å−1 per HST pixel surface (SHSTCamera), i.e. (16) \begin{equation} \tilde{B}_{\rm F} = \frac{\tilde{\phi }_F }{S_{\rm SITELLE}}S_{\rm HSTCamera}, \end{equation} with SSITELLE = 0.322 arcsec2, the image $\tilde{B}_{\rm F}$, once properly aligned, can be directly compared with the HST frame. We have made this comparison for three different regions of the FOV (called WFC3, ACS1 and ACS2, shown in Fig. 9) and four different filters (F656N, F658N, F665N and F660N). Histograms of the flux ratio between the integrated frames and the HST frames are presented in Fig. 10 and the first moments of their distributions are listed in Table 3.

2015ApJ...799..149J___2014_Instance_2

With our joint analysis of stellar mass fraction and source size, we find a larger stellar mass fraction than earlier statistical studies. In Figure 2, we compare our determination of the stellar surface density fraction to a simple theoretical model and to the best fit of a sample of lens galaxies by Oguri et al. (2014). The simple theoretical model is the early-type galaxy equivalent of a maximal disk model for spirals. We follow the rotation curve of a de Vaucouleurs component for the stars outward in radius until it reaches its maximum and then simply extend it as a flat rotation curve to become a singular isothermal sphere (SIS) at large radius (see details in the Appendix). The ratio of the surface mass density of the de Vaucouleurs component to the total surface mass density is shown as a dashed curve in Figure 2. We also show as a gray band the best fit for the stellar fraction in the form of stars determined by Oguri et al (2014) in a study of a large sample of lens galaxies using strong lensing and photometry, as well as the best model using a Hernquist component for the stars and an NFW halo for the dark matter with and without adiabatic contraction, also from Oguri et al. (2014). We have used the average and dispersion estimates for the Einstein and effective radii available for 13 of the objects in our sample from Oguri et al. (2014), Sluse et al. (2012), Fadely et al. (2010), and Lehár et al. (2000; see Table 1) as an estimate of RE/Reff in Figure 2. The average value and dispersion of the sample is RE/Reff = 1.8 ± 0.8. This also averages over the different radii of the lensed images. The agreement of our estimates with the expectations of the simple theoretical model and with estimates from other studies (Oguri et al. 2014) is quite good. For comparison, the estimate of Pooley et al. (2012; using the Einstein and effective radii estimates for 10 out of 14 of their objects from Schechter et al. 2014) seems somewhat lower than expected at those radii. The range of stellar mass fractions from MED09 for source sizes in the range 0.3–15.6 light days is also shown in Figure 2. In this case, the discrepancy between our estimate and their reported value of α = 0.05 is completely due to the effect of the source size. Although accretion disk sizes are known to be smaller in X-rays, recent estimates are in the range of 0.1–1 light-days, depending on the mass of the black hole (see Mosquera et al. 2013), and these finite sizes will increase the stellar surface densities implied by the X-ray data. Another possible origin for this discrepancy is that Pooley et al. (2012) use the macro model as an unmicrolensed baseline for their analysis. It is well known that simple macro models are good at reproducing the positions of images, but have difficulty reproducing the flux ratios of images due to a range of effects beyond microlensing. Recently, Schechter et al. (2014) found that the fundamental plane stellar mass densities have to be scaled up by a factor 1.23 in order to be compatible with microlensing in X-rays in a sample of lenses with a large overlap with that analyzed by Pooley et al. (2012). It is unclear how this need for more mass in stars at the position of the images found by Schechter et al. (2014) can be reconciled with the apparently low estimate of mass in stars at those radii by Pooley et al. (2012). Our estimate of the stellar mass fraction agrees better with the results of microlensing studies of individual lenses (Keeton et al. 2006; Kochanek et al. 2006; Morgan et al. 2008, 2012; Chartas et al. 2009; Pooley et al. 2009; Dai et al. 2010) that reported values in the range 8%–25%, and with the estimates from strong lensing studies (see for example Jiang & Kochanek 2007; Gavazzi et al. 2007; Treu 2010; Auger et al. 2010; Treu et al. 2010; Leier et al. 2011; Oguri et al. 2014) which produced stellar mass fractions in the range 30%–70% integrated inside the Einstein radius of the lenses.

2018MNRAS.475.1160H__Tumlinson_et_al._2013_Instance_1

Galaxies are surrounded by vast gaseous haloes which extend well beyond the hosts’ stellar components: Early observations of quasar sight lines attributed the presence of absorption at multiple intermittent redshifts to gaseous haloes of intervening galaxies (e.g. Bergeron 1986; Bergeron & Boissé 1991; Lanzetta et al. 1995; Tripp, Savage & Jenkins 2000; Chen, Lanzetta & Webb 2001). In the past decade, owing to the rise of large spectroscopic surveys of galaxies with well-determined physical properties (e.g. SDSS), all sky UV surveys (e.g. GALEX), and improved sensitivity of UV spectrographs (e.g. COS), studies of the gaseous haloes of galaxies could systematically connect gas absorption properties to galaxy properties in statistically meaningful samples (e.g. Cooksey et al. 2010; Prochaska et al. 2011; Tumlinson et al. 2013; Liang & Chen 2014; Lehner, Howk & Wakker 2015). The aforementioned gaseous haloes are commonly referred to as the circum-galactic medium (CGM) and are ubiquitous in galaxies regardless of mass or star formation activity: even sub-L* galaxies (Bordoloi et al. 2014), and passive galaxies host a CGM (Thom et al. 2012). The current model of the CGM suggests the presence of a clumpy multiphase medium which extends beyond the virial radius of the host galaxy, with a declining radial density profile, containing a substantial amount of gas and metals (e.g. Werk et al. 2013, 2014; Liang & Chen 2014; Lehner et al. 2014, 2015; Prochaska et al. 2017). Observational studies targeting the CGM of L* galaxies showed that the CGM gas content is comparable to the mass of the interstellar medium (ISM; e.g. Chen et al. 2010; Tumlinson et al. 2011; Werk et al. 2014; Prochaska et al. 2017) and correlates positively with ISM properties (Borthakur et al. 2015). Additionally, CGM observations infer a significant amount of metals (e.g. Werk et al. 2013; Peeples et al. 2014) where CGM metallicities can extend to supersolar metallicities (Prochaska et al. 2017). The clumpy multiphase CGM consists of a warm gas T ∼ 104 − 5 K (clumpy in nature) embedded within a hot diffuse T ∼ 106 K medium (e.g. Heitsch & Putman 2009; Armillotta et al. 2017; Bordoloi et al. 2017). The multiphase structure of the CGM is corroborated by the variety of observed ionic species which survive at a vast range of temperatures: While the warm gas hosts the low ionization species (e.g. H i, Si ii, Si iii, C ii, C iv), the hot medium is home for the most highly ionized species (e.g. O vi, O vii). Additionally, the spectral line profiles of absorbers in the CGM can be reproduced by invoking a patchy medium (e.g. Stern et al. 2016; Werk et al. 2016), i.e. multiple high density gas clouds contribute to the optical depth along the line of sight thus leaving their kinematic imprint on the absorption line profile. For a review of the CGM, see Putman, Peek & Joung (2012) and Tumlinson, Peeples & Werk (2017).

2018ApJ...855...26A__Hsu_et_al._2016_Instance_1

Ever since the initial measurements of the cosmic infrared background (CIB; for a review, see Hauser & Dwek 2001) revealed that the amount of energy radiated in the far-infrared (IR) and submillimeter spectral windows is comparable to that measured at ultraviolet (UV) and optical wavelengths, it has been widely recognized that one of the keys to a comprehensive understanding of the star formation history of the universe is the study of the multiwavelength properties of dusty star-forming galaxies (DSFGs), whose integrated radiation produces the CIB. These systems host intense star-forming activity obscured by large columns of dust, which re-emit the UV radiation of young hot stars at longer wavelengths, so that the peak of their rest-frame spectral energy distribution (SED) falls in the far-IR. In the local universe, DSFGs are typically identified as luminous or ultraluminous infrared galaxies (LIRGs/ULIRGs; Sanders & Mirabel 1996), whereas more distant DSFGs’ emission can be redshifted into the submillimeter domain, allowing many to manifest as submillimeter galaxies (SMGs; Blain et al. 2002; Casey et al. 2014). SMGs were first detected with the Submillimeter Common-User Bolometer Array (SCUBA; Holland et al. 1999) on the James Clerk Maxwell Telescope both in blank-field surveys (e.g., Hughes et al. 1998; Barger et al. 1999; Scott et al. 2002; Serjeant et al. 2003; Webb et al. 2003; Coppin et al. 2006) and behind galaxy clusters (e.g., Smail et al. 1997; Chapman et al. 2002; Cowie et al. 2002; Knudsen et al. 2008). With the subsequent advent of comparable single-dish telescopes and larger format instruments covering the 870 μm atmospheric window like the Large APEX Bolometer Camera (LABOCA; Siringo et al. 2009) on the 12 m Atacama Pathfinder Experiment telescope (APEX; Güsten et al. 2006), and more recently SCUBA-2 (Holland et al. 2013), the number of known SMGs is now of the order of a few thousand (e.g., Weiß et al. 2009; Johansson et al. 2011; Chen et al. 2013; Hsu et al. 2016; Geach et al. 2017), and intensive observational efforts have been devoted to understanding their physical properties. In this quest, one of the main challenges has been the coarse (15″–20″) resolution of single-dish observations, which hinders the identification of counterparts at different wavelengths and can result in the blending of multiple, fainter SMGs into a single brighter object. However, the persistence of the local radio–FIR correlation to higher redshifts (e.g., Condon 1992) allows accurate SMG positions from deep radio imaging obtained with the Very Large Array (VLA) at 1.4 GHz and the Australia Telescope Compact Array (ATCA) at 2.1 GHz (e.g., Ivison et al. 1998, 2000, 2002; Smail et al. 2000; Chapman et al. 2002) to be determined, thus enabling the identification of optical and near-IR counterparts, determination of photometric and spectroscopic redshifts (e.g., Chapman et al. 2005), modeling of SEDs, analysis of individual morphologies, and characterization of dust and stellar components (for full reviews of these results, see Blain et al. 2002 and Casey et al. 2014). The general picture derived from such studies is that SMGs are massive, gas-rich galaxies with high IR luminosities ( ) and complex optical/near-IR morphologies, in which respects they resemble the local ULIRG population. However, SMGs have a median redshift z ∼ 2.5 (Chapman et al. 2005) and a significantly higher number density than ULIRGs. Complementary observations with centimeter and (sub)millimeter telescopes have been used as well to study the cool, molecular gas of SMGs (e.g., Carilli & Walter 2013), an effort that has been transformed in recent years thanks to the exceptional spatial resolution and sensitivity provided by the Jansky VLA and the Atacama Large Millimeter/submillimeter Array (ALMA). Moreover, high-resolution continuum imaging at 870 μm with ALMA has made it possible to resolve the structure of the dust emission from SMGs and identify their counterparts in an unbiased way, revealing that a large fraction of bright single-dish detections actually “break up” into multiple, fainter ( mJy) SMGs blended together at the coarse resolution of the maps in which they were detected (Hodge et al. 2013; Karim et al. 2013; Simpson et al. 2015a, 2015b).

2022MNRAS.516.3381J__Lindblom_&_Owen_2002_Instance_1

Studying the dynamical properties of rotating neutron stars is a domain which brings out various interesting features when one assumes a perfect fluid. It is known that the centrifugal force of a rotating star counters gravitational force and hence one can expect massive stars to be fast rotors, at least in the initial stages of the stellar evolution. As a result of rotation a star may experience damping due to unstable oscillations such as the r-modes. The r-modes are one of many pulsating modes that exist in neutron stars and are characterized by the Coriolis force acting as the restoring force (Andersson 1998). The r-modes are unstable to emission of gravitational radiation (GR) by the Chandrashekhar-Friedman-Schutz (CFS) mechanism (Chandrasekhar 1970; Friedman & Schutz 1978). It was shown in Andersson (1998) that the r-modes are unstable for all rotating perfect fluid stars irrespective of their frequency. Dissipative effects such as shear and bulk viscosities work towards suppressing GR driven instabilities and has been studied by various authors over the past few years (Lindblom, Owen & Morsink 1998; Jones 2001; Lindblom & Owen 2002; van Dalen & Dieperink 2004; Drago, Lavagno & Pagliara 2005; Nayyar & Owen 2006; Jaikumar, Rupak & Steiner 2008; Jha, Mishra & Sreekanth 2010; Ofengeim et al. 2019) under various considerations. If the GR time-scale is shorter than the damping time-scale due to such dissipative processes, then the r-mode will be unstable and a rapidly rotating neutron star could lose a significant fraction of its rotational energy through GR. At higher temperatures (T > 109 K), the dominant dissipation is due to bulk viscosity, which arises due to density and pressure perturbations, a consequence of the star being driven out of equilibrium by oscillations. The system tries to restore equilibrium through various internal processes. In the case of r-modes, since the typical frequencies are of the order of the rotational frequencies of the stars, the reactions that dominate are the weak processes. Within these weak processes, although the modified Urca processes involving leptons are important, it has been shown that non-leptonic processes involving hyperons contribute more significantly towards bulk viscosity at temperatures lower than a few times 109 K (Lindblom & Owen 2002). Our goal here is to investigate the same using a chiral model calibrated to reproduce the desired nuclear matter properties, in particular the density content of the nuclear symmetry energy at both low and high densities.