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2019AandA...628A.110M__Kryukova_et_al._(2012)_Instance_3

Deriving the completeness limits of the WISE photometry is mandatory to assess the reliability of our catalogue of starless cores. We examined the histograms of the number of mid-infrared (MIR) sources versus magnitude; taking into account the effects of the cuts required to fulfil the criteria of Koenig et al. (2012), rough completeness limits are [3.6] ~ 14, [4.6] ~ 12, [12] ~ 9 and [22] ~ 7. These values are 1–3 mag brighter than the sensitivity limits quoted in the WISE Explanatory Supplement3 for the relevant sky region. Once converted into flux units and, for example, compared with the models of Class I and Class 0 sources of 0.5 M⊙ by Whitney et al. (2004), it can be seen that the completeness limits at 3.6 and 4.6μm are faint enough to detect such objects taking into account a distance of 700 pc and a further foreground reddening up to AV = 20. Even in the worst case of edge-on discs, these objects would be detectable at 3.6 and 22μm. Furthermore, the completeness limit at 22 μm is faint enough to allow detection of Class I and Class 0 sources of even-lower-mass central objects. Alternatively, one can compute the bolometric luminosity following Kryukova et al. (2012). Starting from our completeness limit at 22 μm, after conservatively dereddening it by AV = 20, we assumed a spectral index γ = −2 (see Table 3 for definition) to compute the MIR luminosity from Eq. (6) of Kryukova et al. (2012). Equation (7) of Kryukova et al. (2012) then yields Lbol ~ 1.7–2.8 L⊙, depending on whether the NIR flux is neglected (which may be the case) or extrapolated from γ = −2. A comparison with the birthline of Palla & Stahler (1993) indicates a mass of ~ 0.4–0.5 M⊙ for the central protostar. For the sake of comparison, we can roughly estimate the completeness limit in central masses of the Herschel protostellar cores in Giannini et al. (2012) using their quoted completeness limit at 70 μm of 0.21 Jy and following Dunham et al. (2008). By using Eq. (2) of Dunham et al. (2008), scaled to a distance of 700 pc, we found that the flux density at 70 μm translates into a bolometric luminosity of the central (proto)star Lbol ~ 0.28 L⊙ (we note that Dunham et al. 2008 indicate this luminosity as Lint). We highlight the fact that the 70 μm emission is in principle a more sensitive protostellar tracer than WISE. However, this contrasts with the much lower number of protostellar cores found by Giannini et al. (2012), which may be due to a poorer effective sensitivity because of their selection criteria.

2020MNRAS.496.3582C__Komacek_&_Showman_2016_Instance_1

3D General Circulation Models (GCMs) with simplified thermal forcing are one possible intermediate step between 3D GCMs with full coupling between radiation and dynamics like those used by Showman et al. (2009) and Amundsen et al. (2016), and shallow water models, i.e. atmosphere models with one atmosphere layer comprising vertically averaged flow (Showman, Cho & Menou 2010). Fully coupled GCMs have the highest accuracy in stellar radiation and flow coupling and thus the highest predictive power. They are, however, computationally much more expensive and their complexity makes it more difficult to test underlying modelling assumptions compared to GCMs with simplified thermal forcing. The latter are thus better suited to run simulations for various scenarios, to understand large-scale flow and circulation properties in 3D climate models under different conditions (Liu & Showman 2013; Mayne et al. 2014; Tsai et al. 2014; Carone et al. 2015, 2016; Komacek & Showman 2016; Hammond & Pierrehumbert 2017; Mayne et al. 2017). Such models have been proven to be very useful: superrotation in hot Jupiters was first inferred by Showman & Guillot (2002) in a 3D GCM with Newtonian cooling. Recently, Showman, Tan & Zhang (2019) used Newtonian cooling to establish a clean, simple environment to diagnose flow dynamics in brown dwarfs, Jupiter, and Saturn-like planets. Shallow water models present an even simpler model framework and represent 3D flow patterns in an atmosphere depth-dependent (2D) formalism (Showman & Polvani 2010, 2011; Penn & Vallis 2017). There are other useful radiative forcing parametrizations such as those using the dual-band radiative scheme, which can also explore a large parameter space and basic assumptions (see e.g. the model used by Komacek et al. 2017). Generally, a hierarchy of models with various levels of complexity has proven to be extremely beneficial to understand complex flow patterns in full 3D climate simulations. Here, we establish a clean, simple environment to understand possible dynamical feedback between the lower boundary and observational flow via Newtonian cooling.

2021ApJ...913...55H__Goldman_et_al._2017_Instance_2

The short-plateau SNe 2006Y, 2006ai, and 2016egz most likely come from partially stripped massive progenitors,36 36 The lack of nebular spectra for SNe 2006Y and 2006ai remains a caveat. but a remaining question is their exact formation channel. If it is single-star evolution as assumed in this work, the main theoretical uncertainties are RSG wind mass-loss rates and stellar rotation (e.g., Hirschi et al. 2004; Georgy 2012; Chieffi & Limongi 2013; Meynet et al. 2015; Renzo et al. 2017). We assume no rotation and tweak the wind efficiency by hand, but it is debatable whether such high mass-loss rates are physically plausible. Observationally, there is indeed a wide range of measured RSG wind mass-loss rates (e.g., de Jager et al. 1988; van Loon et al. 2005; Mauron & Josselin 2011; Goldman et al. 2017; Beasor et al. 2020). In addition, recent observational and theoretical studies on RSGs and SNe II indicate that RSG wind mass-loss rates may be independent from metallicity (Goldman et al. 2017; Chun et al. 2018; Gutiérrez et al. 2018). Thus, it could be possible that the short-plateau SNe 2006Y, 2006ai, and 2016egz originate from single-star evolution. However, it is unlikely the case if RSG mass-loss rates are metallicity dependent (as in the main-sequence O/B stars; e.g., Vink et al. 2000, 2001; Mokiem et al. 2007), given the estimated subsolar host metallicities (Table 2). In such a case, interacting binary evolution is more plausible, as Eldridge et al. (2017, 2018) indeed show some interacting binary products also result in short-plateau SNe. It is also important to note that any mass-loss models need to reproduce the observed populations of not only SNe II but also RSGs. For example, Neugent et al. (2020) recently show that the luminosity function of RSGs can be used to constrain their mass-loss rates. Future statistical studies with both RSG and SN II populations at various metallicities are required to distinguish the formation channels of short-plateau SNe.

2015ApJ...804..130C___2013_Instance_1

We have developed the simplest spherical void lens model based on the recently developed embedded lens theory. We have assumed a uniform mass profile for the void, compensated by a thin bounding shell. The infinitesimally thin bounding shell was chosen for convenience (Maeda & Sato 1983a, 1983b). To investigate other void profiles such as a non-uniform void interior or a finite-thin bounding ridge (Colberg et al. 2005; Lavaux & Wandelt 2012; Pan et al. 2012; Sutter et al. 2012; Hamaus et al. 2014; Kantowski et al. 2015) is straightforward; one has only to evaluate the Fermat potential of Equation (1) or equivalently the potential part of the time delay of Equation (4). It is also possible to build embedded void lens models with non-spherically symmetric density profiles given that the lowest-order embedded lens theory is applicable to any distributed lens (Kantowski et al. 2013). It is well accepted by the lensing community that small overdensities attract light, whereas small underdensities repel light. This fact can be rigorously proved using general relativistic perturbation theory (Sachs & Wolfe 1967) assuming . However, the repulsive nature of lensing by a large and deep underdense region (i.e., cosmic voids) as described by the rigorously derived but simply implemented embedded lens formalism did not appear until Kantowski et al. (2013). In the case of large density contrasts, i.e., approaching its lower bound −1 for cosmic voids, the repulsive lens equation follows naturally from the embedded lensing theory. This theory is based on Swiss cheese models (Einstein & Straus 1945), which are exact solutions of Einstein’s field equations containing inhomogeneities with large density contrasts (Chen et al. 2010, 2011, 2015; Kantowski et al. 2010, 2012, 2013). The void-lensing community takes void repulsive lensing as granted (e.g., Amendola et al. 1999; Das & Spergel 2009), whereas the galaxy/cluster strong-lensing community has ignored embedding effects, i.e., the repulsive lensing caused by the large underdense regions surrounding the central overdense lens. Besides correctly predicting repulsive lensing by cosmic voids, our Fermat potential formulation can be used to compute the void-lensing time delay effects, including the ISW effect caused by voids; see Equation (5).

2019ApJ...887..137S__Vekstein_2017_Instance_1

As mentioned above, the magnetic reconnection is introduced as breaking and reconfiguration of the oppositely directed magnetic field lines in highly conducting plasma. The magnetic field lines collapse near the X-point and form an extended magnetic singularities known as a current sheet. There are two mechanism of the current-sheet formation. The first kind of current-sheet formation is associated with the MHD instabilities (e.g., resistive tearing mode and ideal kink mode) known as spontaneous magnetic reconnection (e.g., White 1984; Baty 2000; Vekstein 2017). The second kind of current sheet can be formed in the MHD stable configuration, where some external perturbations trigger the forced magnetic reconnection (Hahm & Kulsrud 1985). The forced magnetic reconnection may be activated by nonlinear MHD waves, which may be caused by explosive solar activities (e.g., Sakai et al. 1984; Dewar et al. 2013; Beidler et al. 2017). The forced magnetic reconnection may be developed due to boundary perturbations, which induce a surface current in such a way that it opposes the progress of the reconnection (Ishizawa & Tokuda 2000, 2001; Fitzpatrick 2003). The multimode simulation approach has been adopted to investigate the thinning of the current sheet induced by forced magnetic reconnection (Birn et al. 2005). The motion of the photospheric footpoints of the coronal magnetic field may also trigger the forced magnetic reconnection, which may be caused by the explosive solar coronal events (e.g., Vekstein & Jain 1998; Jain et al. 2005; Vekstein 2017). Although there is a remarkable development in the theory of the forced magnetic reconnection, Jess et al. (2010) have suggested that there is no observational evidence of explosive flare or coronal activities triggered by forced magnetic reconnection. They have observed a microflare activity driven by forced magnetic reconnection. The lower solar atmosphere (photosphere & chromosphere) is dominated by cool, partially ionized and collision dominated plasma. Most of the energy releases during the forced magnetic reconnection may be consumed by such plasma systems (e.g., Litvinenko 1999; Chen et al. 2001; Chen & Ding 2006; Litvinenko et al. 2007).

2017AandA...606A..17M__Kennicutt_(1998)_Instance_1

The SFR reported in Table C.1 refers to a stellar mass range from Mlow = 0.1M⊙ to Mup = 100M⊙, is averaged over the past Δt = 100 Myr, and was calculated using the standard SFR(LIR) relationship from Kennicutt (1998; here scaled to a Chabrier 2003, IMF) (1)\begin{equation} \label{eq:sfr} \textit{SFR}=10^{-10}\times L_{\rm IR}[{L}_{\sun}]\, {M}_{\sun}~{\rm yr}^{-1}. \end{equation}SFR=10-10×LIR[L⊙] M⊙yr-1.This calibration relies on the starburst synthesis models of Leitherer & Heckman (1995), and it is based on the assumption of solar metallicity, and an optically thick (τdust ≫ 1) starburst region, in which case LIR is a good proxy of the system’s bolometric luminosity (LIR ≃ Lbol), and hence a sound, calorimetric probe of the obscured, current stellar birth rate. A possible caveat is that the contribution to the dust heating by more evolved stellar populations (the cirrus component; e.g. Helou 1986; Lonsdale Persson & Helou 1987; Walterbos & Greenawalt 1996) is not taken into account. If the cirrus ISM component heated by the more general galactic UV radiation field contributes to LIR, then the Kennicutt (1998) relationship overestimates the SFR. Another issue is the fact that some percentage of the UV photons can escape the starburst region without being absorbed, and hence are not reprocessed into IR photons (indeed, some of our SMGs are visible in the rest-frame UV images; Miettinen et al. 2017b). The MAGPHYS code also gives the SFR as an output, and contrary to the aforementioned LIR diagnostic, the model permits for the heating of the dust by older and longer-lasting stellar populations. We found that the SFR(LIR) is somewhat higher on average than SFRMAGPHYS: the SFR(LIR) /SFRMAGPHYS ratio was found to range from 0.47 to 6.92 with a median of \hbox{$1.31^{+0.83}_{-0.17}$}1.31-0.17+0.83, where the ± errors represent the 16th–84th percentile range (see the corresponding panel in Fig. 2). If, instead of Δt = 100 Myr, the aforementioned comparison is done by using the SFRMAGPHYS values averaged over the past Δt = 10 Myr, the median SFR(LIR) /SFRMAGPHYS ratio is found to be \hbox{$1.15^{+0.38}_{-0.27}$}1.15-0.27+0.38, which is consistent with the results obtained by da Cunha et al. (2015). Unless otherwise stated, in our subsequent analysis we use the SFR averaged over the past 100 Myr as calculated using Eq. (1).

2017ApJ...835....2X___2003_Instance_1

On the other hand, a clear physical interpretation of the observed pulse broadening phenomenon requires a good understanding of the interstellar electron density structure. A power-law model of electron density fluctuations is commonly adopted in theoretical constructions on radio wave propagation (Lee & Jokipii 1976; Rickett 1977, 1990) and is compatible with observational indications (e.g., Armstrong et al. 1995). Recent advances in understanding the properties of magnetohydrodynamic (MHD) turbulence (Goldreich & Sridhar 1995; Lithwick & Goldreich 2001; Cho & Lazarian 2002, 2003) stimulate a renewed investigation on density statistics (Beresnyak et al. 2005; Kowal et al. 2007; Lazarian et al. 2008; Burkhart et al. 2009, 2010, 2015; Collins et al. 2012; Federrath & Klessen 2012), which provide important insight into key physical processes such as star formation in the turbulent and magnetized ISM (see reviews by, e.g., McKee & Ostriker 2007; Lazarian et al. 2015). The density spectrum in compressible astrophysical fluids was systematically studied in Kowal et al. (2007) by carrying out an extensive set of MHD numerical simulations with varying compressibility and magnetization. Instead of a single Kolmogorov slope with a power-law index of , significant variations in the spectral slope of density fluctuations are present. For supersonic turbulence, their results are consistent with earlier findings in both magnetized (Beresnyak et al. 2005) and nonmagnetized (Kim & Ryu 2005) fluids. It shows that the density power spectrum becomes shallower as the sonic Mach number ( ) increases, where VL is the turbulent velocity at the outer scale of turbulence and cs is the sound speed in the medium, and there is a significant excess of density structures at small scales in highly supersonic turbulence. This behavior is naturally expected as the gas is compressed in shocks by supersonic flows and the interacting shocks produce local density enhancements (Mac Low & Klessen 2004; Padoan et al. 2004b).

2018AandA...614A..86G__Hamann_et_al._(2006)_Instance_1

In the supersonic part of WR outflows, the presence of instabilities and inhomogeneities should be taken into account. Invoking density inhomogeneities, or “clumping”, in stellar models has an influence on the mean opacity, due to the enhanced density in clumps (Moffat et al. 1988; Hamann & Koesterke 1998). However, a porous structure could also imply lowered mean opacity, counteracting the effect of small-scale clumping (Shaviv 1998; Oskinova et al. 2007). Assuming that the material near the Fe-opacity peak is clumped, Gräfener et al. (2012) were able to extend the surface radii of their hydrostatic helium star models, in practical terms, by significantly enhancing the iron bump opacity (see also Appendix A). The surface temperatures of such hydrostatic, strongly inflated models computed with plane parallel grey atmospheres are then compared with the fictitious effective temperatures at τ = 20 of the atmosphere calculations performed by Hamann et al. (2006). This effective temperature at τ = 20 should not be confused with the actual blanketed temperature at the base of WNE wind models, which is about a factor of 2 larger (see e.g. Appendix D). As we show in Sect. 4, strongly inflated hydrodynamic solutions imply supersonic flows already at the base of the inflated envelopes for the typical mass-loss rates of WNE stars (cf. the plane parallel atmosphere model in Fig. 4). Moreover, it would be inconsistent to think that the iron opacity bump is simultaneously responsible for both the inflation of the envelope and the acceleration of the flow. While the detailed effects of clumping and porosity remain a subject for future research, we concentrated in this work on the homogeneous case as those instabilities are expected to initiate above the sonic point (Sundqvist et al. 2013; Owocki 2015). If this is not the case and clumping is already present at the relatively high densities and optical depth of the sonic point of massive helium star models, it might affect to some extent the local opacity.

2021MNRAS.508.4332M__Draine_1978_Instance_1

As a first step, we need to specify the photoelectron sheath features. In this course, we first evaluate the steady state potential over the lunar surface (equation 4), and then after, we use this as a boundary condition to solve the Poisson equation (equation 2) and estimate the photoelectron sheath profile. In calculations, Lyman α (λ ∼ 121.57 nm, 10.29 eV, Λ ∼ 3 × 1011 cm–2 s–1) spike of solar photon radiation (Bauer 1973) is considered as the dominant source for the generation of photoelectrons from the lunar surface. The work function of the regolith material is taken from Grobman & Blank (1969), where it is suggested to vary in the range ϕr ∼ (4–6) V for the region across the subsolar point and limb. Moreover, Draine’s formulation is accounted to determine the lunar surface’s photoelectric efficiency (Draine 1978; Draine & Salpeter 1979) – its spectral dependence can be represented as ${\chi _{\nu r}} = {\chi _o}[1 - ({\phi _r}/{E_\nu })]$. For instance, for the Lyman α radiation χνr = 0.042 for optimum efficiency χo = 0.1 (Sickafoose et al. 2001) and ϕr = 6 V (Grobman & Blank 1969). Another significant parameter is the surface temperature which describes the electron population within the lattice available for the photoemission. Lunar Reconnaissance Orbiter based measurements (Williams et al. 2017) suggest that the surface temperature may vary from the equator (∼400 K) to the terminator (poles, ∼150 K). In order to take this account, we use the latitude (θ) dependent empirical relation ${T_\theta } = {T_0}[1 - (5/4\pi )\theta ]$; for instance, at θ = 70°, and To ≈ 205 K. These three parameters, viz., ϕr, To, χν, and Λ drive the photoemission current from the lunar regolith. The nominal solar wind plasma parameters are considered for calculating collection current over lunar regolith; the constituents are considered as40-41nes ≈ nis = 8.7 cm–3 and Tes ≈ Tis = 1.4 × 105 K (Mann et al. 2011; Kureshi et al. 2020). These solar radiation and wind plasma parameters might vary widely during active solar events and alter surface charging and sheath features. Popel et al. (2018) suggest the dust number density may also vary in a wide range depending on lunar altitude and particle size; for instance, nd ∼ 800 cm–3 for the particles of size 100 nm ≤ ao ≤ 200 nm and θ = 77°. Note that the secondary electron emission (Seitz 1940; Misra, Mishra & Sodha 2013) from the lunar regolith (and floating dust) is ignored, as it minimally contributes to the charging of sunlit surfaces (Mishra & Bhardwaj 2020). These parameters, along with equation (4), yield steady state potential over the sunlit locations. This estimate of the surface potential is used as a boundary condition (i.e. at l = 0, υ = υo) alongwith υ’ = υ = 0 as $l \to \infty $ to solve the Poisson equation (equation 2) numerically – using this framework, the sheath structure is derived in terms of electric potential (υ), electric field (Es), and photoelectron population density (npe).

2015ApJ...815...15W__Dib_&_Kaspi_2014_Instance_1

1E 1841–045, located at the center of supernova remnant (SNR) Kes 73 (Vasisht & Gotthelf 1997), is another steady X-ray emitter. The source was monitored with NuSTAR between 2013 September 5 and 21, during which time six bursts were detected (An et al. 2015). In order to eliminate the contamination from the SNR, an annulus region with inner and outer radii of 60″ and 100″ around the source position was selected for background spectral extraction, as was also done in An et al. (2013). We searched for soft X-ray data from 2013 August 1 to October 30, and identified 11 Swift XRT observations, 10 executed in WT mode and 1 short observation in Photon Counting (PC) mode. Note that in WT mode, 10 rows are compressed into a single row, only the central 200 columns are read, and only 1D imaging is preserved. Consequently, it is impossible to resolve the SNR contribution and three SNR related emission lines (Mg, Si, and S) clearly appear in the stacked spectrum of 10 WT data. Since the source emission has been stable for more than 15 years (Dib & Kaspi 2014), we performed the joint fit with the longest, burst-free NuSTAR observations (ObsID = 30001025012) and XMM-Newton observations that were performed ∼11 years earlier. For the XMM-Newton observation, since the MOS1/MOS2 data were performed in the full frame mode and were seriously affected by pile up, we only use the pn data in the following spectral fitting. The joint spectra are fit by our model (STEMS3D+PL) and a 2% systematic error is added to the XMM-Newton data to account for calibration uncertainties. The fit results yielded that 1E 1841–045 has the lowest twist angle (Δϕ ∼ 0.83) and the highest electron velocities in the magnetosphere (β ∼ 0.28) among the four sources studied here. The resulting photon index of the PL component (Γ ∼ 1.23) is consistent with the value reported based on INTEGRAL data (Kuiper et al. 2006). However, due to the short exposure of the XMM-Newton data, in addition to the high interstellar absorption, the parameter values have large uncertainties (see Table 2 and Figure 2). Note that 1E 1841–045 is the only source whose non-thermal component (FP ∼ 5.1 × 10−11 erg s−1 cm−2) dominates the STEMS3D component (FS ∼ 3.6 × 10−11 erg s−1 cm−2) in the range of 1–79 keV.

2019MNRAS.488.5029H__Malhotra_et_al._2001_Instance_1

For the first time, we detected [C ii] 158-μm emission from a GRB host galaxy at z > 2. This is the second detection of [C ii] 158-μm emission among known GRB host galaxies, following GRB 980425 (Michałowski et al. 2016). The [C ii] 158-μm fine structure line is the dominant cooling line of the cool interstellar medium, arising from photodissociation regions (PDR) on molecular cloud surfaces. It is one of the brightest emission lines from star-forming galaxies from FIR to metre wavelengths, almost unaffected by dust extinction. [C ii] 158-μm luminosity, L[C II], has been discussed as an indicator of SFR (e.g. Stacey et al. 2010). If L[C II] scales linearly with SFR, the ratio to FIR luminosity, L[C II]/LFIR, is expected to be constant, since LFIR is a linear function of SFR (e.g. Kennicutt 1998a). However, LC II/LFIR is not constant, but declines with increasing LFIR, known as the ‘[C ii] deficit’ (e.g. Luhman et al. 1998, 2003; Malhotra et al. 2001; Sargsyan et al. 2012; Díaz-Santos et al. 2013, 2017; Spilker et al. 2016). The [C ii] deficit persists when including high-z galaxies (e.g. Stacey et al. 2010; Wang et al. 2013; Rawle et al. 2014). In Fig. 5, we compare the [C ii] deficit in the GRB 080207 host and other star-forming galaxies. Two GRB hosts are shown by stars: GRB 080207 (orange star) and 980425 (blue star). The comparison sample is compiled from the literature up to z ∼ 3 (Malhotra et al. 2001; Cormier et al. 2010, 2014; Ivison et al. 2010; Stacey et al. 2010; Sargsyan et al. 2012; Farrah et al. 2013; Magdis et al. 2014; Brisbin et al. 2015; Gullberg et al. 2015; Schaerer et al. 2015). Active galactic nuclei are separated from star-forming galaxies based on either (i) the explicit description in the literature or (ii) EWPAH 6.2μm 0.1 (Sargsyan et al. 2012). As reported by previous studies (e.g. Maiolino et al. 2009; Stacey et al. 2010), high-z galaxies are located at a different place from local galaxies in the L[C II]/LFIR–LFIR plane.

2016AandA...591A..38C__Roediger_et_al._(2011a)_Instance_1

Despite the considerable scatter in both colors and color gradients (Peletier & Balcells 1996; Taylor et al. 2005; Roediger et al. 2011a), the tight correlation between color and stellar mass of the host galaxies holds true in both the region identified as intermediate and outer in Sect. 7. ETGs form a tight red sequence (see Fig. 10) for both regions and show an average inside-out gradient of ~0.1 mag (Fig. 12, bottom). LTGs form instead two different distributions: the blue cloud of the intermediate (or bulge/bar) region becomes red (i.e., it reaches the red sequences) above 1010 M⊙ while the outer, disk-dominated region never overlaps completely the red sequence. Still, more massive disks are redder than their lower mass counterparts but the difference between the colors of the outer and the intermediate region increases with respect to the total stellar mass. This can be possibly induced by the growth of a red and dead structure in the center of massive disks, i.e. that the central part of galaxies underwent a star formation quenching process that turned them red. Our results on the average properties of color profiles broadly agree with literature data. For example MacArthur et al. (2004) have shown that the radial profile of the average ages of the stellar populations decreases from inside out and that the steepness of the decrease is a function of morphological type. Color templates shown in Fig. 9 exhibit a radial behavior fully consistent with the average age profiles shown by MacArthur et al. (2004). We find there is also a good agreement with the (g−H) color profiles published in Roediger et al. (2011a) for almost all the morphological types, although in their median profiles of early disks they find positive color gradients (and consistently positive age population gradients in their stellar population analysis, Roediger et al. 2011b) that we do not see. Roediger et al. (2011b) do not find any direct link between galaxy morphologies and the observed stellar population gradients. On the contrary, studies such as Cheung et al. (2013) and Gavazzi et al. (2015) have shown that the bar occupation fraction rises steeply above 109.5 M⊙ (as also confirmed by the works done by Skibba et al. 2012; Masters et al. 2012) and that, above this mass,galaxies are progressively more quenched (red) in their centers, while their disks still sustain SF and hence are blue. These studies therefore highlight that the presence of structures such as bars can indeed produce the color gradients that we observe and likely also the stellar population gradients. These authors thus conclude that a secular bar drives the quenching of the star formation in the central kiloparsecs of galaxies. Moreover massive galaxies undergo bar instability earlier than their lower mass counterparts and thus have more time to grow redder than low mass systems. Moreover, Méndez-Abreu et al. (2012) on a study of the Virgo bar fraction have shown that this rises up to more than 50% above 1010 M⊙, adding a further link between color/stellar populations radial gradients that we observe and structures such as bars (Laurikainen et al. 2010). We stress, however, that disk instabilities can also rejuvinate the central stellar population by, e.g., triggering central star formation in correspondence of the ILRs of spirals or bars (an example could be the one of VCC 508 in Fig. 3a).

2015AandA...581A..31S__Giodini_et_al._2013_Instance_1

Although the origin and evolution of linear-scale clustering is well described by the concordance model (Spergel et al. 2007), gravitational clustering of matter on smaller scales (galaxy clusters and groups) belongs to a non-linear regime of structure formation. This regime is more difficult to understand and to simulate because its evolution must include the role of baryons, which are driven by complex physics. Clusters of galaxies that are the most massive gravitationally bounded structures have been widely used over the past years to probe the cosmic evolution of the large-scale structures in the Universe (Voit 2005; Allen et al. 2011). In the standard model of structure formation driven by gravitation alone, clusters form a self-similar population that is only characterized by their mass and redshift. Including baryon physics introduces some distortions in the scaling relations between the mass and other physical quantities such as temperature, X-ray, or optical luminosity (Kaiser 1986; Giodini et al. 2013). Most recent research works have focused on the relationship between the dominant dark matter and the baryonic matter that forms gas and stars (Lin et al. 2003; Giodini et al. 2009). Both the mass-to-light (M/L) ratio of structures and the halo occupation number (HON, or the number of satellite galaxies per halo) correspond to observables that are easy to compare to predictions from numerical simulations (Cooray & Sheth 2002; Tinker et al. 2005). They are both representative of the way stellar formation occurred in the early stages of halo formation (Marinoni & Hudson 2002; Borgani & Kravtsov 2011). Recent progress on numerical simulations (Murante et al. 2007; Conroy et al. 2007; Aghanim et al. 2009) has also stressed the role of hierarchical building of structures in enriching the intra-cluster medium (ICM) with stars in a consistent way with the observed amount of ICM globular clusters and ICM light. This ICM light, although hardly detectable, can be considered as the extension of the diffuse envelope often seen in the central galaxy in rich clusters of galaxies. It is an important component, although not the only one, that explains the formation of the brightest cluster galaxies (BCG) in the centre of clusters of galaxies (Dubinski 1998; Presotto et al. 2014).

2018ApJ...867..123M__Helling_&_Fomins_2013_Instance_1

Regardless of their exact composition, particles suspended in these exoplanet environments likely undergo repeated particle–particle collisions in response to atmospheric circulation. Such dynamics have been inferred to drive efficient triboelectrification (e.g., Helling et al. 2013), resulting in electrified cloudy or hazy environments at elevation. As happens within Earth’s clouds, exoplanet clouds are likely gravitationally stratified, meaning that smaller, lighter grains become concentrated at the top of the clouds, while larger, heavier grains remain at lower elevation (see Figures 2 and 8 in Helling et al. 2008; also Helling & Fomins 2013; Helling et al. 2016). Because, as discussed above, the polarity of charge collected by particles from triboelectric processes depends on grain size, this stratification (smaller particles at elevation; larger particles on the bottom) has the ability to set up coherent electric fields. Such charge separation occurs in both thunderstorms and volcanic plumes and ultimately drives spark discharges–lightning–either through conventional breakdown of the gas or via runaway electron avalanche (Kikuchi & Endoh 1982; Gurevich et al. 1992; James et al. 1998; Dwyer 2005; Cimarelli et al. 2014; Dwyer & Uman 2014; Aizawa et al. 2016). On Earth, these discharges support a global electric circuit and have the ability to modulate chemical and physical reactions in the atmosphere (Price 1993; Rakov & Uman 2007; Siingh et al. 2007; Genareau et al. 2015; Wadsworth et al. 2017; Mueller et al. 2018). Indeed, lightning may have been involved in the production of prebiotic molecules in an early-Earth environment (Miller & Urey 1959; Navarro-González et al. 1998) and such lightning has been hypothesized to have been associated within dusty flows (namely, volcanic plumes) rather than hydrometeor clouds (Navarro-González et al. 1998; Segura & Navarro-González 2005; Johnson et al. 2008). If the mineral clouds inferred to exist on extrasolar worlds can be considered analogs to the dusty environments in our own solar systems, charging within these systems may also stimulate a wide array of electrostatic phenomena and help catalyze prebiotic chemistry (Hodosán et al. 2016).

2022AandA...663A...5M__Andrae_et_al._2018_Instance_1

With the real-time pipeline, we also frequently detect eclipsing binaries, due to their significant changes in brightness. Often, when we trigger these sources, PV light curves reveal potentially eclipsing behaviour. In these cases, we can attempt to verify the nature of the variable source by creating Lomb-Scargle periodograms for the light curves38 and inspecting, by eye, the folded light curves that we create using the best periods found. We note that this is done independently of the rest of pipeline; it is not part of the automatic software. An example of an eclipsing binary we found using TUVOpipe is shown in Fig. 32. The strong variability (by over 1 optical magnitude) led us to create a long-term light curve; the PV light curves of all the used UVOT filters are shown in Fig. 33. The source is a known source that appears in many sky survey catalogues, including Gaia, where its G-band magnitude is listed as 14.1 and its inferred temperature is ~5000 K (see Andrae et al. 2018 for how the listed temperature we extract from the Gaia catalogue is inferred from the Gaia data). This G-band magnitude is brighter than the U-UV bands seen in the UVOT light curve, indicating (along with the temperature) that it is a relatively red source. The source is also present in the ATLAS catalogue of variable stars Heinze et al. (2018), where it is denoted as AT J098.1962+05.6837. The ATLAS catalogue gives a period of 1.985 days. However, no classification of the source was so far known, so the cause of the found periodicity is not clear. Our method gave a best period of 1.9899 days, so we used this period to fold the UVOT U-band light curve, which is shown in Fig. 34. The light curve clearly reveals an eclipse in which the source becomes dimmer by around 1.5 magnitudes, and the shape of the light curve confirms it as an eclipsing binary. There may also be additional very weak minima seen halfway between the deep eclipses (see around phase −0.5 and 0.5 in the plot shown). The fact that any potential secondary minima are very weak may suggest that this is an Algol-type system (these are known as EA binaries; see Carmo et al. 2020 for a review of these systems and example light curves; the inferred Gaia temperature is also consistent with this being an EA system).

2015ApJ...815...39W__Frank_et_al._1996_Instance_1

With improved resolution, in this work we have resolved corrugated swept-up shells as well as small blobs of mixed material in the wind region that were not seen in Paper I. The swept-up shells are much smoother in the simulations including poloidal field, which is related to the stabilization of shear instability by the magnetic field. To check how an even higher resolution can affect the results, we have run simulations for case b2 at a resolution of 3200 × 990. In the upper panel of Figure 9 we present the density map at t = 1000 year for the run without ambient poloidal field. A close comparison to its ordinary resolution counterpart (see the upper left panel of Figure 5) shows that the details of the mixing structures are quite different in the two runs. More complex and fragmented mixing structures are generated in the higher resolution simulation, which indicates that the process producing these structures is not completely resolved at the current resolution (see, e.g., Frank et al. 1996). Despite the different fine structures, the overall outflow shape and the jet structure in the two simulations are in good agreement. In the lower panel of Figure 9 we present the same maps for the run with ambient poloidal field. The smoother and thicker shell seen here is almost identical to its ordinary resolution counterpart (see the lower left panel of Figure 5), and similar feather-like structures are also present near the wind boundary. There is also no sign of intense stochastic behavior like that seen in the upper panel of Figure 9. This suggests that the stabilization of the wind-ambient interface by the poloidal field is quite robust, and this conclusion holds well at least for the resolution we have used. Finally, it should be noted that the wind in the current simulations fans out in all radial directions even at the equator where an accreting inner envelope or disk is instead expected. Whether this setup could have anything to do with the growth of stochastic mixing structures near the inner toroid will need to be resolved in future simulations with more realistic setup of boundary conditions.

2021ApJ...908...95H__Sanders_&_Mirabel_1996_Instance_1

Star-forming galaxies at redshifts z ∼ 1–3 probe the cosmic epoch when most of the stellar mass assembly in the universe took place (Madau & Dickinson 2014, and references therein). A better understanding of star formation (SF) during this epoch is therefore imperative to understand SF across cosmic time. Locally, less than 5% of the galaxy population has a star formation rate (SFR) that is significantly higher than the empirical main sequence for star-forming galaxies, i.e., the tight correlation (∼0.3 dex) between the SFR and stellar mass, M⋆ (Brinchmann et al. 2004; Elbaz et al. 2007, 2011; Noeske et al. 2007; Goto et al. 2011; Rodighiero et al. 2011; Sargent et al. 2012; Whitaker et al. 2012, 2014; Salmon et al. 2015). These often-called starburst galaxies, with an IR luminosity LIR ∼ (0.1–5) × 1012 L⊙ (e.g., Sanders & Mirabel 1996; Downes & Solomon 1998), become increasingly more common at high z. In fact, (sub)millimeter number counts reveal that galaxies with LIR > 1012–13 L⊙, at z > 0.5, are many hundreds of times more likely to exist than in the local universe (Blain et al. 2002; Chapman et al. 2005; Berta et al. 2011; Magnelli et al. 2011; Béthermin et al. 2012; Magnelli et al. 2013; Casey et al. 2013, 2014; Geach et al. 2013; Simpson et al. 2014; Strandet et al. 2016; Brisbin et al. 2017). Meanwhile, the cosmic molecular gas density also peaks at z ∼ 1–3 (Decarli et al. 2014, 2016a, 2016b, 2019; Walter et al. 2014; Lentati et al. 2015; Pavesi et al. 2018; Liu et al. 2019; Riechers et al. 2019). This suggests a strong link between molecular gas and SF. Rest-frame far-IR (FIR) measurements of spectral lines and thermal dust continuum emission have been used to investigate the cooling and heating processes of the interstellar medium (ISM) in star-forming galaxies; however, the physical conditions at high z are still, in general, poorly investigated (Popesso et al. 2012; Bothwell et al. 2013; Carilli & Walter 2013; Genzel et al. 2013; Yang et al. 2017; Tacconi et al. 2018, 2020; Aravena et al. 2020; Birkin et al. 2020; Boogaard et al. 2020; Lenkić et al. 2020).

2020ApJ...895..128M__Zaldarriaga_et_al._2018_Instance_1

We analyze the 10 BBH mergers reported by LIGO and Virgo in their O1 and O2 observing runs (Abbott et al. 2019a; LIGO Scientific Collaboration & Virgo Collaboration 2019). Before discussing results, it is useful to review expectations from the literature for the spin distributions resulting from different formation scenarios. Isolated binary evolution is predicted to yield black holes with spins preferentially aligned with their orbit. Although spin misalignments may be introduced by natal supernova kicks, episodes of mass transfer and tidal torques serve to realign component spins before the formation of the final black hole binary (Rodriguez et al. 2016; Zevin et al. 2017; Gerosa et al. 2018; Qin et al. 2018; Zaldarriaga et al. 2018; Bavera et al. 2020). The black holes’ spin magnitudes in this scenario are much more uncertain. Recent work indicates that angular momentum is efficiently transported away from stellar cores, leaving black holes with natal spins as low as a ∼ 10−2 (Qin et al. 2018; Fuller & Ma 2019). While tides on the progenitor of the second-born black hole can spin up the progenitor star (Zaldarriaga et al. 2018), this effect can be counteracted by mass loss in stellar winds, and more detailed simulations find only low or moderate spin increases due to tides (Qin et al. 2018; Bavera et al. 2020). Meanwhile, dynamically formed systems in dense stellar clusters have no a priori preferred axis, and so are likely to have random spin configurations (Rodriguez et al. 2016, 2018, 2019; Doctor et al. 2020). Once again, however, the expected spin magnitudes are largely unknown, subject to the same uncertainties mentioned above regarding natal black hole spins. One firm prediction of the dynamical scenario concerns the spins of second-generation binaries, whose components were themselves formed from previous mergers. Regardless of their component spins, black hole mergers generally yield remnants with a ∼ 0.7; thus the effective spin of two such second-generation binaries may be large (Fishbach et al. 2017; Gerosa & Berti 2017; Rodriguez et al. 2018, 2019; Doctor et al. 2020).

2019MNRAS.483.2362R__Calderone_et_al._2013_Instance_1

Various types of AGNs are known and one among them are the narrow-line Seyfert 1 (NLSy1) galaxies, which are classified based on the presence of narrow Hβ emission line with full width at half-maximum (FWHM) 2000 km s−1 and weak [O iii] emission line, with F([O iii])/F(Hβ) 3 (Osterbrock & Pogge 1985; Goodrich 1989). They are believed to be powered by low-mass black holes (${\sim } 10^7 \, \mathrm{M}_{\odot }$) having higher accretion rate and generally showing strong Fe ii emission compared to their broad line counterparts namely the broad-line Seyfert 1 (BLSy1) galaxies (Xu et al. 2012; Rakshit et al. 2017a). However, from spectro-polarimetric observations of a γ-ray emitting NLSy1 galaxy, PKS 2004 − 447 (Baldi et al. 2016) and accretion disc modelling of a sample of 23 radio-detected NLSy1 galaxies (Calderone et al. 2013) indicate that they have masses similar to the blazar class of AGNs. Other characteristics that make NLSy1 galaxies different from the BLSy1 galaxies are their rapid soft X-ray variability (Pounds, Done & Osborne 1995; Leighly 1999a), steep soft X-ray spectra (Boller, Brandt & Fink 1996; Wang, Brinkmann & Bergeron 1996; Leighly 1999b) and low amplitude optical variability (Grupe 2004; Rakshit & Stalin 2017). Also, the fraction of NLSy1 galaxies detected in radio is much lower (${\sim } 7{{\ \rm per\ cent}}$) compared to the fraction of radio detected BLSy1 galaxies (Komossa et al. 2006; Rakshit et al. 2017a). Among radio-loud NLSy1 galaxies, about a dozen (∼2 per cent) have been detected in γ-ray by the Fermi-Large Area Telescope (e.g. Abdo et al. 2009; D’Ammando et al. 2015; Paliya et al. 2018) suggesting the unambiguous presence of relativistic jets in them. Multiband broad-band SED modelling of these γ-ray detected NLSy1 galaxies indicate that these sources have many properties similar to the blazar class of AGNs (Paliya et al. 2013b) and specifically resembling the flat spectrum radio quasar (FSRQ) category (Paliya et al. 2018). In the radio, these γ-ray emitting NLSy1 galaxies have a compact core jet morphology, high brightness temperature, show superluminal motion and significant radio variability (Doi et al. 2006; Komossa et al. 2006). Detailed investigations of the population of NLSy1 galaxies need to be undertaken to understand more about their peculiar characteristics.

2021MNRAS.507.6012Z__Kendrick_2018_Instance_2

Being a benchmark system H + H2, H + HD, and their isotopic counterparts have received much attention over the last several decades (Marinero et al. 1984; Zhang & Miller 1989; D’Mello et al. 1991; Harich et al. 2002; Gao et al. 2015; Yuan et al. 2018a, b, 2020). Most early experimental and theoretical investigations were centered around benchmarking theory against experiments and providing improved descriptions of the H3 potential energy surfaces (PES; Boothroyd et al. 1996; Mielke, Garrett & Peterson 2002; Yuan et al. 2018a, b). Among the available PESs for the H3 system, the one by Boothroyd et al. (1996) referred to as the BKMP2 PES and by Mielke et al. (2002) referred to as the CCI PES, nearly equally well account for most experimental data for H + H2, H + HD, and D + HD collisions. These PESs have also been able to account for even subtle effects such as the GP (Kendrick et al. 2015; Hazra et al. 2016; Croft et al. 2017; Kendrick 2018, 2019). The GP effect, while not significant at temperatures relevant to astrophysics, is important below 1 K as illustrated in a series of calculations on H + HD (v, j) collisions for vibrational levels v = 4 − 9 (Kendrick et al. 2015; Hazra et al. 2016; Croft et al. 2017; Kendrick 2018, 2019). Though several prior studies of Flower and co-workers (Flower 1999, 2000; Flower & Roueff 1999; Wrathmall et al. 2007) have reported rate coefficients for H + HD collisions, due to the approximations involved in the scattering calculations (e.g. neglect of hydrogen atom-exchange), the reliability of the available rate coefficients has been a source of debate (Desrousseaux et al. 2018). Recently, Desrousseaux et al. (2018) reported rate coefficients for pure rotational transitions for j ≤ 10 within the v = 0 vibrational level using accurate quantum calculations that include the exchange channel. In this paper, we report rate coefficients for state-to-state rovibrational transitions in HD induced by H atoms between and within the v = 0 and 1 vibrational levels and for temperatures ranging from T = 1–1000 K.

2015MNRAS.453.2747M__Shaw_et_al._2008_Instance_1

ΓCR can be expressed as the product of three factors – the total cosmic ray ionization rate per H nucleus ζH (including both primary and secondary ionizations), the average energy deposited into the medium per ionization ΔQ, and nH. Both ζH and ΔQ are rather uncertain and can vary considerably over different Galactic environments. Typical values of ζH in dense gas are ∼1–5 × 10−17 s−1 (Dalgarno 2006), but there is evidence from H3+ observations that ζH is considerably higher in the diffuse gas under consideration here (Dalgarno 2006; Indriolo & McCall 2012). Indriolo & McCall (2012) find a mean ζH of 1.8 × 10−16 s−1 in their sample of diffuse molecular sight lines, and values as large as ∼1 × 10−15 s−1 have been reported in the literature (Snow & McCall 2006; Shaw et al. 2008). In this paper, we adopt the value 1.8 × 10−16 s−1. For ΔQ, we use 10 eV, as estimated for diffuse molecular gas from table 6 of Glassgold, Galli & Padovani (2012), although it is important to note that this value can vary by several eV depending on the precise physical and chemical conditions in the cloud. Combining these factors, the cosmic ray heating rate is (11) \begin{eqnarray} \Gamma _{\rm {CR}} &=& \zeta \Delta Q n_{\rm {H}} \nonumber \\ &\approx& 1.9 \times 10^{-25} \left( \frac{n_{\rm {H}}}{30 \hspace{3.0pt} \rm {cm}^{-3}} \right) \hspace{3.0pt} \rm {ergs} \hspace{3.0pt} \rm {cm}^{-3} \hspace{3.0pt} \rm {s}^{-1}. \end{eqnarray} For ΓPE, we adopt the expression: (12) \begin{equation} \Gamma _{\rm {PE}} = 1.3 \times 10^{-24} \hspace{3.0pt} n_{\rm {H}} \epsilon G_0 \hspace{3.0pt} \rm {ergs} \hspace{3.0pt} \rm {cm}^{-3} \hspace{3.0pt} \rm {s}^{-1} \end{equation} from Wolfire et al. (2003), where G0 is the intensity of FUV light in units of the Habing (1968) field and ϵ is the heating efficiency factor given by equation 20 of Wolfire et al. (2003). For nH = 30 cm−3, T = 100 K, an electron fraction of 1.6 × 10−4, and a FUV field of G0 = 1.1 (Mathis, Mezger & Panagia 1983), ϵ evaluates to 1.8 × 10−2, yielding (13) \begin{equation} \Gamma _{\rm {PE}} = 7.6 \times 10^{-25} \left( \frac{n_{\rm {H}}}{30 \hspace{3.0pt} \rm {cm}^{-3}} \right) \hspace{3.0pt} \rm {ergs} \hspace{3.0pt} \rm {cm}^{-3} \hspace{3.0pt} \rm {s}^{-1}. \end{equation}

2022AandA...658A.188S__Liu_et_al._(2013)_Instance_1

The trends between the LF slope α and the aforementioned parameters, with the addition of the morphological T type, are shown in Fig. 4 together with their Spearman correlation coefficient (ρ) and their p value, indicating the probability that the two sets of data are uncorrelated. We summarize the properties for which we look for a correlation in Table 4. We define a correlation to be negligible when |ρ|=[0 − 0.2], weak when |ρ|=[0.2 − 0.4], moderate when |ρ|=[0.4 − 0.6], strong when |ρ|=[0.6 − 0.8], and very strong when |ρ|=[0.8 − 1]; using the p value to evaluate the probability that, despite showing a correlation, two variables may be uncorrelated. It should be noted that only a handful of studies so far have looked at the correlation between α and global galaxy properties: Kennicutt et al. (1989), Elmegreen & Salzer (1999), Youngblood & Hunter (1999), van Zee (2000), and Thilker et al. (2002) investigated nebular LFs as in this paper, while Liu et al. (2013) identified H II regions via Paα, and Cook et al. (2016) studied the GALEX far-ultraviolet (FUV) LFs of H II regions. While the sample of Cook et al. (2016) includes a few hundred galaxies, the other studies are based on samples ranging from 10 to 35 galaxies, similar to our study. In this section and in Sect. 6.1, we compare our results to those studies that, as in our case, applied a uniform analysis methodology on galaxy samples. It should be noted that using different tracers means probing different source ages and, as reported by Oey & Clarke (1998), older H II regions tend to have steeper LF slopes, mainly due to the short main-sequence lifetimes of the more massive stars constituting the brighter H II regions. This is the reason why, for example, FUV observations, probing H II regions with ages less than 100 Myr, are expected to deliver a steeper LF compared to Hα observations, typically probing H II regions younger than 10 Myr, and our comparison remains qualitative.

2019MNRAS.490.5478W__Winter_et_al._2018b_Instance_1

A growing body of work suggests that planet formation is strongly dependent on the birth environment of the host star. Stars preferentially form in groups (Lada & Lada 2003), and in sufficiently dense environments the evolution of a PPD can be significantly influenced by neighbours (de Juan Ovelar et al. 2012). Close star–disc encounters are one such environmental influence on PPDs that can result in enhanced accretion and hasten disc depletion (Clarke & Pringle 1993; Ostriker 1994; Pfalzner et al. 2005; Olczak, Pfalzner & Spurzem 2006; Bate 2018; Winter et al. 2018a; Cuello et al. 2019). However, the stellar number densities required for tidal truncation are high, and in practice few observed regions satisfy this condition (Winter et al. 2018b, 2019a). The influence of tidal truncation is therefore limited to stellar multiples, either in bound systems (Dai et al. 2015; Kurtovic et al. 2018) or during the decay of higher order multiplicity (Winter, Booth & Clarke 2018c). Since stellar multiplicity does not appear to be strongly dependent on environment (see Duchêne & Kraus 2013, for a review), this suggests that encounters are not an environmental influence, but may set disc initial conditions during the early phases of cluster evolution (Bate 2018). Discs can also be externally depleted via thermal winds driven by far-ultraviolet (FUV) and extreme ultraviolet (EUV) photons from neighbouring massive stars (Johnstone, Fabian & Taylor 1998; Störzer & Hollenbach 1999; Adams et al. 2004; Facchini, Clarke & Bisbas 2016; Haworth et al. 2018; Haworth & Clarke 2019). This process of external photoevaporation dominates over dynamical encounters in observed environments, and can deplete PPDs rapidly for many stars that are born in massive and dense clustered environments (Scally & Clarke 2001; Winter et al. 2018b). Many stars in the solar neighbourhood are born in regions where UV fields are sufficient to significantly shorten disc lifetimes (Fatuzzo & Adams 2008; Winter et al. 2018b), and the fraction of stars born in such environments may be much greater outside of this region, dependent on galactic environment (Winter et al. 2019a). From an observational perspective, Guarcello et al. (2016) report disc survival fractions that decrease with increasing FUV flux in Cygnus OB2 (see also Winter, Clarke & Rosotti 2019b), and Ansdell et al. 2017 find a correlation between the dust mass in PPDs and separation from σ Ori. However, Richert et al. (2015) find no correlation of disc fraction with distance from OB stars. Reconciling these contradictory findings may require appealing to the inefficiency of external photoevaporation at small radii within the disc, dynamical and projection effects, or the stellar age gradient apparent in many star forming regions (Getman et al. 2018).

2018MNRAS.478.4357S__Barr_&_Hochberg_1988_Instance_1

Since long cosmologists have felt motivated to look for alternative explanations for the DE beyond a rigid cosmological constant Λ. The scalar field paradigm was then profusely used also to make the cosmic vacuum dynamical: Λ = Λ(ϕ). In the old days, the main aim was to adjust the large value of Λ typically predicted in QFT to be zero. There were many early proposals (see e.g. Endo & Fukui 1977, 1982; Fujii 1982; Dolgov 1983; Abbott 1985; Zee 1985; Barr 1987; Ford 1987; Peccei, Solà & Wetterich 1987; Weiss 1987; Barr & Hochberg 1988). In spite of the hopes raised by these works at solving the ‘old CC Problem’, it was later shown in Weinberg (1989) through the so-called no-go theorem that most if not all the dynamical adjustment mechanisms existing in the literature to date were plagued by more or less obvious forms of subtly hidden fine tuning. For this reason, the subsequent use of scalar fields in cosmology was mostly focused on trying to explain another aspect of the CCP: the cosmic coincidence problem (viz. the fact that $\rho _\Lambda$ happens to be so close to the matter density ρm right now; see e.g. Peebles & Ratra 2003). The new wave of dynamical scalar fields in cosmology crystalized in the notions of quintessence, phantom fields and the like, which have had a tremendous influence in cosmology till our days (see e.g. Peebles & Ratra 1988; Ratra & Peebles 1988; Wetterich 1988; Wetterich 1995; Caldwell, Dave & Steinhardt 1998; Zlatev, Wang & Steinhardt 1999; Amendola 2000; Caldwell, Kamionkowski & Weinberg 2003), the reviews (Sahni & Starobinsky 2000; Padmanabhan 2003; Peebles & Ratra 2003; Copeland, Sami & Tsujikawa 2006), and the many references therein. At the same time a blooming crest of models based on ascribing a direct phenomenological time-dependence to the CC term, Λ = Λ(t), broke with impetus into the market. For an account of some of the old attempts, see Overduin & Cooperstock (1998, and references therein).

2016ApJ...817..173L__Takahashi_2004_Instance_1

Horizon scale imaging promises to test basic predictions of GR and improves our understanding of the physics responsible for accretion and emission in a strong gravitational field. In particular, imaging a black hole shadow has been a long-standing goal of black hole astronomy. However, imaging the black hole shadow feature in Sgr A* has been inherently challenged by two known effects. First, the scattering by interstellar medium blurs the strong GR features near the black hole. In a recent work, it has been shown that this effect can be mitigated based on the fact that the scattering is well understood over the relative range of baseline lengths provided by the EHT (Fish et al. 2014). Second, while the predicted shadow feature is nearly independent of the spin or orientation of the black hole to within 10% (Bardeen 1973; Takahashi 2004), the emission region surrounding the black hole depends on the details of the underlying accretion process and is intrinsically time variable primarily due to the stochastic nature of magnetorotational-instability-driven turbulence and magnetic reconnection in the accretion flow. Magnetorotational instability (MRI, Balbus & Hawley 1991, 1998) is believed to be the leading mechanism driving turbulence in accretion disks and develops on orbital timescales. The timescale for the Keplerian motion at the innermost stable circular orbit around the black hole in Sgr A* ranges from 30 minutes for a non-rotating black hole to 4 minutes for prograde orbits around a maximally rotating black hole (Doeleman et al. 2009b). These timescales are much less than the typical duration of a Very Long Baseline Interferometry (VLBI) experiment, which violates one of the basic requirements for VLBI Earth-rotation aperture synthesis imaging. In contrast, the corresponding timescales in the nearby giant elliptical galaxy M87, which has the second largest apparent event horizon, are much larger (a minimal timescale of a few days).

2015ApJ...801..112L__Kulsrud_1983_Instance_1

The main goal of this paper was to further expand the recent transport theory of Zank et al. (2014) and thus, by default, earlier attempts by Drake et al. (2006, 2013) and Bian & Kontar (2013). In our approach, similar to Zank et al. (2014), the focus was on inertial-scale flux ropes that were modeled as quasi-2D magnetic islands superposed transversely on a strong large-scale magnetic field imbedded in the large-scale plasma flow, as suggested by the 3D MHD turbulence simulations with a strong guide field of Dmitruk et al. (2004). We adopt a different perspective in proceeding from the basic guiding center kinetic equation (e.g., Kulsrud 1983) instead of the transformed Vlasov equation (Skilling 1975) used by Zank et al. (2014). This offers the advantage of explicitly identifying drift acceleration for various guiding center drift mechanisms and the betatron acceleration that energetic particles would be subject to in both the large-scale plasma flow and magnetic field, and in the inertial-scale plasma flow and magnetic fields of contracting and merging flux ropes. Our approach allows us to distinguish between particle drift and betatron energization when magnetic flux ropes behave in an incompressible fashion (later stage contraction or during merging) or when they are compressible (early stage contraction and during island collisions). The transformation of the guiding center kinetic equation for nearly gyrotropic particle distributions into a focused transport equation clarifies the role of drift and betatron acceleration in focused transport. In the process, a more general focused transport formalism of particle transport and momentum change in contracting and merging flux ropes was derived, whereas Zank et al. (2014) followed a more targeted focused transport approach based on specific conservation laws (magnetic moment conservation, conservation of parallel action, and conservation of magnetic flux) that are thought to apply to the above-mentioned three acceleration mechanisms (see also Drake et al. 2006, 2013).

2021MNRAS.508.4512L__Fishbach_et_al._2019_Instance_1

An EM counterpart is not strictly necessary to use compact binary mergers such as BBHs and BNSs as standard sirens. By matching the sky localization region of GW sources – which can be inferred from the GW measurements – with galaxy catalogues, one might in fact be able to extract complementary information on the redshift of the sources, without the need of spotting an EM counterpart. The idea was originally proposed by Schutz (1986) and it has subsequently been used and developed in different analyses (Holz & Hughes 2005; MacLeod & Hogan 2008; Petiteau, Babak & Sesana 2011; Del Pozzo 2012; Chen et al. 2018; Gray et al. 2020). It has already been tested with real data collected by the LIGO and Virgo detectors (Fishbach et al. 2019; Soares-Santos et al. 2019; Palmese et al. 2020; Abbott et al. 2020d; Finke et al. 2021), though the constraints obtained so far with this ‘statistical’ method are not competitive with the ones derived from GW170817 and its EM counterpart, mainly because of the poor spatial resolution of the current network of ground-based interferometers. Future observations, taken with an enlarged network of ground-based GW detectors, will allow for better cosmological measurements (Chen et al. 2018), mainly thanks to the improved sky localization accuracy. Other complementary methods, which analogously do not require the identification of an EM counterpart, might yield interesting results as well (Taylor & Gair 2012; Oguri 2016; Del Pozzo, Li & Messenger 2017; Mukherjee & Wandelt 2018; Farr et al. 2019; Mukherjee et al. 2020a; Mukherjee, Wandelt & Silk 2020b, c, d; Ezquiaga & Holz 2021). The era of precise cosmological measurements with GWs will however start only with next-generation interferometers, such as the Einstein Telescope (ET) (Punturo et al. 2010; Sathyaprakash, Schutz & Van Den Broeck 2010; Belgacem et al. 2019a; Maggiore et al. 2020) and the Cosmic Explorer (Abbott et al. 2017a; Reitze et al. 2019a, b) on the Earth, or TianQin (Mei et al. 2020), Taiji (Luo et al. 2020), and the Laser Interferometer Space Antenna (LISA) (Amaro-Seoane et al. 2017) in space. The latter instrument is the focus of the present investigation. In what follows, we will briefly introduce LISA and review previous studies of LISA’s capability to do cosmological analyses using standard sirens. More details on how to extract cosmology from GWs by statistically matching with galaxy catalogues will be given in Section 2.

2019MNRAS.487.3776P__Moriondo,_Giovanardi_&_Hunt_1998_Instance_1

Driven by the influx of spatially resolved observations coming from integral field units (IFU), more recent investigations have focused on attempting to infer structural, dynamical, and/or chemical properties for localized regions of galaxies, by decomposing them into physically motivated components. In fact, this concept pre-dates the large-scale use of IFU with works dealing with, for instance, photometric disc/bulge decompositions of surface brightness profiles (e.g. see Kent 1985; Cinzano & van der Marel 1993; Scorza & Bender 1995; Moriondo, Giovanardi & Hunt 1998; Krajnović et al. 2013), including using multiple filters (Dimauro et al. 2018). Aside from surface brightness, radial profiles of other parameters have also been subject to analogous decompositions. For instance, the decomposition of gas (usually H i) circular velocity profiles into contributions from different galaxy sub-components is a well-established practise (as in van Albada et al. 1985; Carignan, Sancisi & van Albada 1988; Battaglia et al. 2006; Noordermeer et al. 2007; Swaters et al. 2012; Aniyan et al. 2016, 2018; Sofue 2017), whilst decompositions of mass profiles have attempted to infer the contributions from dark matter (DM) and baryons (stars and globular clusters, gas, et cetera; Annunziatella et al. 2017; Poci, Cappellari & McDermid 2017; Bellstedt et al. 2018). These concepts have been extended to two dimensions, including multiband photometric disc/bulge decompositions of images, rather than profiles (for instance, see Scorza et al. 1998; de Souza, Gadotti & dos Anjos 2004; Norris, Sharples & Kuntschner 2006; Simard et al. 2011; Méndez-Abreu et al. 2017; Dalla Bontà et al. 2018). Moreover, there have been recent efforts to conduct the decomposition directly on an observed spectrum (Johnston et al. 2012; Coccato et al. 2015; Tabor et al. 2017; Coccato et al. 2018), to similarly determine the contributions to various spectral features coming from ‘distinct’ galaxy sub-components. This type of component-based approach attempts to isolate the distinct contributions to observed galaxies from regions that may or may not have had different origins and/or formation paths; however, they have thus far dealt with the problem from only one perspective – dynamics or stellar populations.

2017AandA...601A.109W__Hoyt_et_al._1994_Instance_1

The WSN/ISN series is based on the counts of both sunspot groups and individual sunspots, with the former being weighted with a factor of ten: (1)\begin{equation} R = k\cdot(10\cdot G + S), \end{equation}R=k·(10·G+S),where G and S are the numbers of sunspot groups and individual sunspots, respectively, and k is a correction factor, characterizing each observer. However, resolving individual spots may be imprecise with poor instrumentations, and a new series, based only on sunspot groups, was proposed, called the group sunspot number, GSN (Hoyt et al. 1994; Hoyt & Schatten 1998). The GSN is more robust than WSN regarding observational conditions (e.g., Usoskin 2017). There is still a potential problem related to the grouping of individual spots, which may have been considered by earlier observers in a different manner to that currently accepted (Clette et al. 2014). This uncertainty is related to both WSN/ISN and GSN but can be fixed by redefining groups in historical sunspot drawings (Arlt et al. 2013). The GSN series produced by Hoyt & Schatten (1998) also uses the linear scaling and daisy-chaining method to reduce different data to the same reference observer, for which the RGO was chosen. The GSN is constantly scaled up by a factor 12.08 to make it comparable with the WSN series. The main advantage of the GSN series is that Hoyt & Schatten (1998) had collected and published the original database of raw data, including all the records of individual observers. This makes it possible to revise the entire series if needed. Since some corrections and additions have been recently made to this dataset, a revised database of the sunspot group numbers, separate for each observer, is published (Vaquero et al. 2016, referred to as V16 hereafter). The GSN series was revised by Svalgaard & Schatten (2016) who performed a full re-calibration of the observers using a modified daisy-chaining method with a reduced number of links (the “backbone” method). The revised backbone GSN series suggests that the level of solar activity was relatively high in the 18th and 19th centuries, much higher than that implied by the original GSN series by Hoyt & Schatten (1998) and by WSN.

2015ApJ...800...62B___2014b_Instance_1

We find that, even though the physical parameters such as kTe and τe are very well constrained by the data, it is still impossible to formally distinguish the geometry. The slab (disk-like) and the spherical geometries, as parameterized by the compTT and compPS models used here, both describe the MCG–05-23-016 spectrum equally well. We note that a similar result was found in observations of the Seyfert 1.2 IC 4329a and the narrow-line Seyfert 1 SWIFT J2127.4+5654 with NuSTAR (Brenneman et al. 2014a, 2014b; Marinucci et al. 2014). Both of these AGNs and MCG–05-23-016 are radio-quiet, however, they differ in other properties. With a mass of the super-massive black hole of ∼5 × 107 M (Wandel & Mushotzky 1986), the mean intrinsic 2–10 keV luminosity of 1.66 × 1043 erg s−1 (see Table 1) and a bolometric correction from Marconi et al. (2004), MCG–05-23-016 is accreting at approximately 5% of the Eddington rate. This is almost an order of magnitude less than the key other two AGNs. Interestingly, SWIFT J2127.4+5654 has the lowest black hole mass and the lowest cut-off, followed by MCG–05-23-016 in the middle, and IC 4329a with highest mass and cut-off energy. In a number of other AGNs, a stringent lower limit on the cut-off energy was placed using the NuSTAR data, indicating a generally higher coronal temperature and lower optical depths, e.g., Ecut > 190 keV in 3C 382 (Ballantyne et al. 2014) and in Ark 120 (Matt et al. 2014), and Ecut > 210 keV in NGC 2110 (Marinucci et al. 2015). Using long-term averaged data from INTEGRAL, Malizia et al. (2014) constrained cut-off energies for 26 AGNs in the range between 50 and 200 keV, some of which have been or will be observed with NuSTAR. With more high-quality measurements in the near future, covering a wide range of physical properties, it will be possible to directly probe the physics of the AGN corona. In order to distinguish the fine differences due to the coronal geometry, longer observations of sources with a weaker reflection continuum will be needed.

2021AandA...656A..15G___1974_Instance_1

The comparison between Ulysses and Pamela overlapping measurements revealed that the proton flux in the rigidity interval 1.6–1.8 GV (0.92–1.09 GeV, corresponding approximately to the median energy of the GCR spectrum at solar minimum) has a radial intensity variation of 2.7 ± 0.2% AU−1, and a latitudinal gradient of −0.024 ± 0.005% degree−1 (De Simone et al. 2011). Positive (negative) latitudinal gradients are observed during positive (negative) polarity periods. In addition, Experiment 6 (E6) on board Helios-A and Helios-B provided ion data from four to several hundreds of MeV n−1 (Winkler 1976; Marquardt & Heber 2019). The Helios-A and Helios-B S/C were launched on December 10, 1974 and January 15, 1976 during a positive polarity epoch and were sent into ecliptic orbits of 190-day and 185-day periods around the Sun. The orbits perihelia were 0.3095 AU and 0.290 AU, respectively. The aphelia were approximately 1 AU. As a result, the Helios data are representative of the cosmic-ray bulk variations that are to be experienced by Solar Orbiter, which will also reach maximum distances from the Sun of about 1 AU. In the recent paper by Marquardt & Heber (2019), the Helios proton data radial gradients of the GCR flux were found to be 6.6 ± 4% above 50 MeV and 2 ± 2.5% between 250 and 700 MeV between 0.4 and 1 AU. These results are in agreement with those from Pamela/Ulysses (within the statistical and systematic errors). In conclusion, variations in the GCR proton-dominated flux along the Solar Orbiter orbit are expected to be of a few % at most; consequently, it is plausible to assume that models for cosmic-ray modulation developed on the basis of observations gathered near Earth will also apply to Solar Orbiter. On the other hand, the Metis data will allow us to verify this assumption in the unexplored region of tens of degrees above the solar equator. Analogously, even though no SEP data were gathered up to present time, it is likely that a study of the evolution of SEP events near the Sun above the solar equator will be possible for the first time also with Metis, in addition to the dedicated instruments flown on board Solar Orbiter.

2019AandA...631A..35B__Bridges_et_al._(1996)_Instance_1

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...818...38S__Vanzella_et_al._2011_Instance_1

As described in the introduction, spectroscopically confirmed Lyα emitters at high redshift (and the nonconfirmations from follow-up campaigns) have proven very valuable for studying the early universe and the environment at the epoch of reionization (Pentericci et al. 2011, 2014; Caruana et al. 2012, 2014; Treu et al. 2012, 2013; Faisst et al. 2014; Tilvi et al. 2014). In particular, confirmed Lyα emitters fix the redshift of the object, resulting in improved prediction power from fitting stellar population synthesis models to the photometry. Assuming a set of stellar population models (e.g., Bruzual & Charlot 2003; Maraston 2005) to generate spectral energy distributions for galaxies at the emission-line redshift, and fitting them to the available photometry, can give estimates of physical quantities of the galaxies like total stellar mass (the normalization between the observed flux and best-fit model), the star formation rate, metallicity, and the age of the stellar populations, i.e., the galaxy (e.g., Labbé et al. 2006; Vanzella et al. 2011; Coe et al. 2013, 2014; Finkelstein et al. 2013; Huang et al. 2015; Oesch et al. 2015; Zitrin et al. 2015b). When performing the spectral energy distribution fitting, assuming a dust law can furthermore predict the dust content of the galaxy. This can be directly compared to the measured UV spectral slope, if available from the data (e.g., Bouwens et al. 2015; Finkelstein et al. 2015b; Oesch et al. 2015). Another direct comparison can be obtained from independently determining the star formation rate from scaling relations with the UV photometry (Kennicutt 1998; Madau et al. 1998). As part of our study of IRAC-detected high-redshift galaxies presented by Huang et al. (2015), we estimated the physical properties of the confirmed Lyα emitter presented in Section 7.1. An important aspect of this study was the availability of ancillary Spitzer photometry from SURFS-UP (Bradač et al. 2014). Photometry in the rest-frame optical falling in the Spitzer IRAC infrared bands for high-redshift galaxies has proven to be an important part of reliably predicting the physical properties of (high-redshift) galaxies through spectral energy distribution fitting (e.g., Schaerer & de Barros 2010; Labbé et al. 2013; Smit et al. 2014a, 2014b; Finkelstein et al. 2015a; Huang et al. 2015; Wilkins et al. 2015). Furthermore, fixing the redshift of the spectral energy distributions when fitting to photometry can also be used as a test of the validity of potential low-redshift contaminants. If, for instance, the best-fit low-redshift model predicts a dusty red and old stellar population, it would be very unlikely to see strong [O ii] emission, therefore making a high-redshift Lyα scenario more likely (Coe et al. 2013; Finkelstein et al. 2013). Similar arguments can be used to rule out other line-emitting low-redshift contaminants. Fixing the redshift of high-redshift sources behind massive clusters, like the ones presented in the current study behind the GLASS clusters, is not only important for the study of individual sources and high-redshift galaxy populations. Knowing the redshift, i.e., the luminosity distance to any object, precisely, especially if it is multiply lensed, is also very valuable for lens modeling of the foreground clusters (e.g., Coe et al. 2013, 2014; Zitrin et al. 2014). Lastly, the sizes of high-redshift galaxies have also been shown to provide useful information about the environment and epoch they inhabit (Ono et al. 2013; Curtis-Lake et al. 2014; Holwerda et al. 2015).

2019MNRAS.485.3088C__Cheng_et_al._2018_Instance_1

As displayed in Fig. 1 and Supplementary Movie 1, the control torque τ, applied to the lander at rest, actuates it to pivot over the leading edge, and then the bottom side is dragged downwards in contact with the granular regolith, yielding a reaction force on to the lander (Allen et al. 2013). Given sufficient acceleration impulsive moment, the lander will leave the surface, hopping forwards in a ballistic trajectory. Hockman et al. (2017) stated that the key mechanism of this internally actuated mobility is the transmission of rotational energy to translation energy, indicating that the acceleration duration T of the control torque could determine the hopping outcomes. To elucidate the fundamental dynamics that governs surface locomotion on asteroids, we adopt 12 different acceleration durations ranging from 20 to 800 $\rm {ms}$ in this section, in which μ is set to 0.5, β is set to 1.0, and c is set to 0 Pa. Two typical dynamics data, corresponding to 20 and 600 $\rm {ms}$, are displayed in the insets of Figs 2(a) and (b), which shows instantaneous velocity $v$ and angle θ (relative to horizon), versus time t. Generally, during the hopping process, the acceleration first shows a gradual increase, and then decreases rapidly at the end of control torque, during which the reaction force is highly fluctuating due to intermittent transmissions of acoustic energy along force-chain-like networks (Clark, Kondic & Behringer 2012; Cheng et al. 2018), as illustrated in Fig 3. In this collisional scenario, the acceleration torque excites strong force chains which exert sporadic impacts on the bottom of the lander, while after the end of acceleration, the force networks become sparse and fragmented immediately, leading to the rapid decrease of the reaction force as evidenced by Fig. 2(c). The work done by the lander to impact with regolith surface generates the contact force, and then increase its velocity and height before the lander loses contact with the regolith at time tesc. After that, the lander behaves as free projectile motion under only gravity, which corresponds to a gradual decrease of the vertical velocity, forming a peak value in the velocity curve and a turning point in the angle curve. Hereafter, we define these two values as hopping velocity V and hopping angle ϑ, respectively. Note that the lowercase $v$ and θ represent the instantaneous velocity and angle, while the uppercase V and ϑ represent hopping outcomes. The hopping distance can be deduced from V and ϑ, given as 2V2cos (ϑ)sin (ϑ)/g. Thus in the case of T = 600 ms, the lander hops about 57 m under the gravity of 1.0 × 10−4$\rm {m\, s^{-2}}$, corresponding to a large surface coverage on small asteroids. And in a lower acceleration duration like T = 40 ms, the lander only moves about 45 cm, which gives a much more precise form of surface relocation. The results presented here provide strong evidence on the feasibility of the hopping landers for the exploration of low-gravity bodies. Additionally, our simulations show the lander takes about 1–17 min to complete the hop, which is far less than the battery life of MASCOT (16 h, Ho et al. 2017). This information is crucial for the design of landers equipped without solar batteries.

2022AandA...661A.129S__Rodríguez-Almeida_et_al._2021_Instance_2

Radio astronomy is recognized as one of the most effective techniques to search for interstellar molecules. By comparing the spectra of candidate molecules in the laboratory with the spectra observed in astronomical surveys, we can determine whether these molecules exist in interstellar space. Therefore, it is necessary to provide rotational spectra of candidates for astronomical detection. Radio astronomy has helped to detect several sulfur-containing molecules in the ISM in recent years: in particular, thiols, the sulfur analogs of alcohols. Methanethiol (or methyl mercaptan, CH3SH) was detected in the Sagittarius B2 (Sgr B2) region of the center of our Galaxy (Linke et al. 1979; Gibb et al. 2000; Müller et al. 2016; Rodríguez-Almeida et al. 2021) and in the protostar IRAS 16293-2422 (Majumdar et al. 2016). Two groups reported to have detected several signs of ethanethiol (C2H5SH) in Sgr B2 (Müller et al. 2016) and Orion (Kolesniková et al. 2014). Most recently, Rodriguez-Almeida reported the first unambiguous detection of ethanethiol in the ISM, toward the G+0.603-0.027 molecular cloud (Rodríguez-Almeida et al. 2021). Moreover, several sulfur-containing species have been observed in comets (Altwegg et al. 2017). Some recent efforts, both from spectroscopy and astronomical searches, to detect S-stitutes of other classes of compounds have also been reported. For instance, thioformic acid (HC(O)SH) was very recently detected in G+0.693–0.027. Its trans-isomer has an abundance of ~1 × 10–10 (Rodríguez-Almeida et al. 2021). Conversely, thioformamide (NH2CHS), the counterpart of for-mamide (NH2CHO), was characterized in the laboratory up to 660 GHz, and its transitions were searched for toward the hot cores Sgr B2(N1S) and Sgr B2(N2), but it was not detected (Motiyenko et al. 2020). The rotational spectrum of thioac-etamide was recently analyzed in the 59.6–110.0 GHz frequency region (5.03–2.72 mm). Its emission was searched for in regions associated with star formation using the IRAM 30 m ASAI observations toward the prestellar core L1544 and the outflow shock L1157–B1. The molecule was not detected, but the study allowed placing constraints on the thioacetamide abundances (Maris et al. 2019; Remijan et al. 2022).

2021AandA...655A..25Z__García-Burillo_et_al._2014_Instance_1

Outflows are ubiquitous in both luminous AGN and in local Seyfert galaxies, and occur on a wide range of physical scales, from highly ionised semi-relativistic winds and jets in the nuclear region at subparsec scales to galactic scale outflows seen in mildly ionised, molecular, and neutral gas (Morganti et al. 2016; Fiore et al. 2017; Fluetsch et al. 2019; Lutz et al. 2020; Veilleux et al. 2020, and references therein). In some cases molecular and ionised winds have similar velocities and are nearly co-spatial, suggesting a cooling sequence scenario where molecular gas forms from the cooling of the gas in the ionised wind (Richings & Faucher-Giguere 2017; Menci et al. 2019). Other AGN show ionised winds that are faster than the molecular winds, suggesting a different origin of the two phases (Veilleux et al. 2020, and references therein). The molecular phase is a crucial element of the feeding and feedback cycle of AGN because it constitutes the bulk of the total gas mass and it is the site of star formation activity. On galactic scales massive molecular winds are common in local Seyfert galaxies (e.g. Feruglio et al. 2010; Cicone et al. 2014; Dasyra et al. 2014; Morganti et al. 2015; García-Burillo et al. 2014, 2017, 2019); these winds likely suppress star formation (i.e. negative feedback) as they reduce the molecular gas reservoir by heating or expelling gas from the host-galaxy ISM. In late-type AGN-host galaxies, the gas kinematics appears complex at all scales, showing several components such as bars, rings, and (warped) discs, with high velocity dispersion regions (e.g. Shimizu et al. 2019; Feruglio et al. 2020; Fernández-Ontiveros et al. 2020; Alonso-Herrero et al. 2020; Aalto et al. 2020; Audibert et al. 2020). Accurate dynamical modelling of the molecular gas kinematics reveals kinematically decoupled nuclear structures, high velocity dispersion at nuclei, trailing spirals, and evidence of inflows and AGN-driven outflows. (e.g. Combes et al. 2019; Combes 2019, 2021). The outflow driving mechanism (wind shock, radiation pressure, or jet), their multiphase nature, and their relative weights and impact on the galaxy ISM are still open problems (Faucher-Giguère & Quataert 2012; Zubovas & King 2012; Richings & Faucher-Giguere 2017; Menci et al. 2019; Ishibashi et al. 2021). To date, far different outflow phases have been observed only for a handful of sources. Atomic, cold, and warm molecular outflows have been observed in radio galaxies (e.g. Morganti et al. 2007; Dasyra & Combes 2012; Dasyra et al. 2014; Tadhunter et al. 2014; Oosterloo et al. 2017). The nuclear semi-relativistic phase and the galaxy-scale molecular phase have been observed simultaneously in less than a dozen sources, with varied results: in some cases data suggest energy driven flows (Feruglio et al. 2015; Tombesi et al. 2015; Longinotti et al. 2018; Smith et al. 2019), in other cases data suggest momentum driven flows (e.g. García-Burillo et al. 2014; Feruglio et al. 2017; Fluetsch et al. 2019; Bischetti et al. 2019; Marasco et al. 2020). Fiore et al. (2017), using a compilation of local and high redshift winds, showed that there is a broad distribution of the momentum boost, suggesting that both energy- and momentum-conserving expansion may occur. Enlarging the sample of local AGN-host galaxies with outflows detected in different gas phases is important to understand the nature and driving mechanisms of galaxy-scale outflows.

2019ApJ...872...52C__Linsky_2017_Instance_1

Studies aiming at measuring and modeling solar radiation and its variability are strongly motivated by the impact that solar irradiance (that is, the electromagnetic energy emitted by the Sun received at the top of Earth’s atmosphere in units of area and time), especially in the UV, has on the chemistry and physical properties of Earth’s atmosphere and climate (e.g., Gray et al. 2010; Matthes et al. 2017). Studies of solar variability have been recently also driven by the necessity of improving our understanding of stellar variability (see Fabbian et al. 2017, for a recent review), which, in turn, is essential to characterize the habitable zones of stars and the atmospheres of their exoplanets. As for Earth, modeling of exoplanet atmospheres requires as fundamental input the spectral energy distribution of the hosting star, especially UV and shorter wavelengths (e.g., Tian et al. 2014; Ranjan et al. 2017; Rugheimer & Kaltenegger 2018). Unfortunately, measurements of UV radiation are strongly hampered by the interstellar medium absorption (up to 70%–90%), which is significant even for relatively close stars, so that estimates of stellar UV radiation strongly rely on modeling (see Linsky 2017, for a recent review). Moreover, because there is no mission scheduled in the near future to observe stellar spectra in the UV, after the Hubble Space Telescope ceases operations, the characterization of UV spectra of stars hosting exoplanets that will be discovered by current and future missions (e.g., TESS or James Webb Space Telescope) will necessarily rely on indirect estimates, performed, for instance, through the use of semi-empirical models (e.g., Mauas et al. 1997; Fontenla et al. 2016; Busá et al. 2017) or proxies (e.g., Stelzer et al. 2013; Shkolnik et al. 2014). Stellar irradiance variability also affects the detectability of exoplanets (see, e.g., the recent review by Oshagh 2018). The passage over the disk of spots and faculae may induce photometric variations of amplitude similar to or larger than photometric variations induced by planetary transits. Moreover, the presence of active regions may alter spectral line profiles, thus hindering exoplanet detections performed through radial velocity measurements. Similarly, spectroscopic techniques that allow us to estimate the physical properties of exoplanet atmospheres (see, e.g., Kreidberg 2017, for a recent view) require as fundamental input the spectra synthetized through models representing quiet and active regions (faculae and sunspots). Finally, stellar irradiance variability observed at different spectral ranges, especially in the UV, is a fundamental observable for the characterization of the magnetic activity of a star, and therefore for the understanding of dynamo processes in stellar objects (e.g., Reinhold et al. 2013; Basri 2016; Salabert et al. 2016). Because stellar photometric and spectral variability can be modeled using the semi-empirical approaches developed for the Sun described above (see, e.g., Shapiro et al. 2016; Witzke et al. 2018), understanding the limitations of current irradiance models is fundamental to improving our capability of modeling stellar variability.

2015ApJ...806..118S__Moffatt_1978_Instance_1

Magnetic fields observed in various astrophysical systems, such as the Earth, the Sun, disk galaxies, accretion disks, etc., possess large-scale magnetic fields in addition to a fluctuating component. The magnetic field survives for timescales much larger than the diffusion timescales in those systems, and therefore are thought to be self-sustained by turbulent dynamo action. The standard model of such a turbulent dynamo producing a large-scale magnetic field involves the amplification of seed magnetic fields due to the usual α effect, where α is a measure of the net kinetic helicity in the flow (see, e.g., Moffatt 1978; Parker 1979; Krause & Rädler 1980; Brandenburg & Subramanian 2005; Brandenburg et al. 2012). Since it is not necessary for the turbulent flow always to be helical, it is interesting to study the dynamo action in non-helically forced shear flows. Dynamo action due to shear and turbulence in the absence of the α effect received some attention in the astrophysical contexts of accretion disks (Vishniac & Brandenburg 1997) and galactic disks (Blackman 1998; Sur & Subramanian 2009). The presence of large-scale shear in turbulent flows is expected to have significant effects on transport properties (Rädler & Stepanov 2006; Rüdiger & Kitchatinov 2006; Leprovost & Kim 2009; Sridhar & Singh 2010; Singh & Sridhar 2011). It has also been demonstrated that the mean shear in conjunction with rotating turbulent convection gives rise to the growth of large-scale magnetic fields (Käpylä et al. 2008; Hughes & Proctor 2009). The problem we are interested in may be stated as follows: in the absence of the α effect, will it be possible to generate a large-scale magnetic field solely through the action of non-helical turbulence in the background shear flow on the seed magnetic field? This question was recently numerically studied by Brandenburg et al. (2008), Yousef et al. (2008a, 2008b). These works clearly demonstrated the growth of large-scale magnetic fields due to non-helical stirring at small scales in the background linear shear flow.

2020ApJ...893...84B__Almeida_et_al._2014a_Instance_1

Moreover, if mixing is effective over kiloparsec scales, then we would expect the neutral gas observed toward the QSO and the ionized gas seen toward the central H ii region to have the same metallicity. In which case, in order to find , a significant fraction of the neutral gas must be many kiloparsecs away from the star-forming regions in order to not be contaminated. Such an explanation was adopted by Cannon et al. (2005), who postulated the existence of a low-metallicity halo beyond the inner ISM to explain the discrepancy between and toward star-forming regions in NGC 625. Our results are consistent with this idea and provide additional evidence for IGM gas feeding into galaxies through streams from the cosmic web (e.g., Sánchez Almeida et al. 2014a, and references therein). Dwarf galaxy pairs in particular are thought to have enhanced star formation because they are fed by significant reservoirs of neutral gas in which they reside, or because of their mutual interactions (Lelli et al. 2014; Stierwalt et al. 2015; Pearson et al. 2016). As noted in Section 2.4, UGC 5282 does lie near a galaxy of similar mass (UGC 5287 in Figure 3) and interactions between the two galaxies may be the source of gas flowing into UGC 5282. For example, Pearson et al. (2018) have suggested that multiple encounters between dwarf galaxies can “park” gas at significant distances from the protagonists, which can then return over several Gyr. It remains unknown whether the metallicity of such returning debris would be low enough to cause the decrease in metallicity in either UGC 5282 or UGC 5287, but it may reflect whatever buildup of metals occurred in the dwarfs at much earlier times. Alternative tidal models that cause strong metallicity gradients and the removal of low-metallicity gas at the edges of dwarf galaxies (Williamson et al. 2016) seem less well supported by our results. Indeed, the situation for UGC 5282 may be even more complicated, depending on whether it has entered the halo of NGC 3003 (Figure 3) and has begun to feel any effects from ram pressure stripping by gas in the host’s halo. The effects on the metallicity of the dwarf galaxy may, however, be less significant than any effects from tidal stripping (Williamson & Martel 2018).

2018AandA...610A..38F__Bisterzo_et_al._2017_Instance_2

Similarly to the [α/Fe] ratio, the ratio of the slow (s-) neutron capture process elements to iron can be regarded as a cosmic clock. Ba, Sr, La, and Y are mainly s-process elements produced on long timescales by low mass AGB stars (Matteucci 2012). Since a low mass star must evolve to the AGB phase before the s-process can occur, the s-process elements are characterized by a delay in the production, much like the delay of iron production by SNe Ia relative to the α elements production by core collapse SNe. Among the four s-process elements mentioned above, GES provides the abundances of Y II (the first s-process peak element) and Ba II (the second s-process peak element) for all our sample stars. Their abundances behave differently in the Galactic thick and thin discs (Bensby et al. 2005, 2014; Israelian et al. 2014; Bisterzo et al. 2017; Delgado Mena et al. 2017). Unlike the Galactic thick disc stars, which show an almost constant [Ba/Fe] abundance close to the solar value, the Galactic thin disc stars have their [Ba/Fe] abundances increasing with [Fe/H] and reaching their maximum values around solar metallicity, after which a clear decline is seen (see also Cristallo et al. 2015a,b, for the most recent s-process calculation in AGB yields). The same trend is observed in our sample. In Fig. 13 we display the Li-[Ba/Fe], [Ba/Fe] as a function of [Fe/H], and the evolution of absolute Ba abundance A(Ba), as derived from Ba II lines. Similar figures are also plotted for yttrium (Y II). [Ba/Fe] and [Y/Fe] values here are derived from MCMC simulations, taking into account the measurement uncertainties of A(Ba II)/A(Y II) and [Fe/H]. By applying the same MCMC setups used for [α/Fe] (see Sect. 3.1), we calculate the mean values of [Ba/Fe] and [Y/Fe] for each star. These values, together with their corresponding 1σ uncertainties, are listed in Table 1. In the literature there are several theoretical works on the evolution of [Ba/Fe] and [Y/Fe] in the Galactic thin disc (e.g. Pagel & Tautvaisiene 1997; Travaglio et al. 1999, 2004; Cescutti et al. 2006; Maiorca et al. 2012; Bisterzo et al. 2017). For comparison, we show in Fig. 13 the predictions of the most recent one (Bisterzo et al. 2017) where the updated nuclear reaction network was used.

2018MNRAS.480.5113M__Murgia_et_al._2009_Instance_1

In this paper we extend previous work based on cosmological simulations by analysing the general magnetic field properties and the diffuse radio halo emission in galaxy clusters in the IllustrisTNG project, a set of cosmological magnetohydrodynamics simulations run with the moving-mesh code arepo (Springel 2010) that include a comprehensive module for galaxy formation physics. The main and novel aspect of our work is the analysis of the diffuse radio emission resulting from radio haloes in galaxy clusters (Feretti & Giovannini 1996; Murgia et al. 2009; Vacca et al. 2011; Feretti et al. 2012). We investigate radio emission from clusters by a detailed comparison with observations, trying to match the current observational constraints and to make predictions for the upcoming radio surveys that will be performed with the new generation of radio instruments such as SKA and LOFAR. The analysis of simulated radio haloes gives us a complementary view on the spatial extent and energy content of magnetic fields in galaxy clusters, since the radio emission is proportional to their strength. As such, the study of radio halo scaling relations (Giovannini et al. 2009; Cassano et al. 2013; Zandanel, Pfrommer & Prada 2014) with the total X-ray power and halo mass may yield important information about the amplification mechanisms of magnetic fields in clusters and the level of turbulence in the ICM. The modelling of radio emission makes it also possible to study the transport of charged particles and their re-acceleration to relativistic speeds, and it constrains the probability of detecting extended radio-emitting structures in a statistical sample of realistic simulated clusters. The comparison of the simulated radio emission with actual observations might also be employed as a useful check for the implementation of the galaxy formation physics modules used to perform the simulations, although our modelling of relativistic particles is rather preliminary and might have a non-negligible impact on the results.

2019ApJ...878...84M__Oppenheimer_et_al._2016_Instance_1

These observations have identified a large reservoir of baryons surrounding galaxies. This circumgalactic gas extends to roughly the virial radius (Shull 2014) and contains a substantial fraction of the baryons (Werk et al. 2014; Bregman et al. 2018) and metals (Peeples et al. 2014) associated with the dark matter halo. The larger column densities of the O+5 ion in the CGM of star-forming galaxies compared to passive galaxies (Tumlinson et al. 2011) has generated great interest in the CGM, because the processes producing this dichotomy may explain why star formation is quenched in massive halos (Blanton et al. 2003; Kauffmann et al. 2003; Schawinski et al. 2014). In cosmological hydrodynamical simulations, the O vi absorbing gas lies behind the halo accretion shock and is maximal in L* galaxies because their virial temperature is close to the temperature T ≈ 105.5 K where the O+5 ionization fraction peaks (Oppenheimer et al. 2016). Feedback from supermassive black holes may suppress the O+5 fraction in the halos of red galaxies relative to the halos of blue galaxies of similar stellar mass (Nelson et al. 2018). The nucleus would not typically still be active by the time its outflow impacted the gas properties at half the virial radius, so differentiating between nuclear activity and halo mass is challenging observationally (Berg et al. 2018). Simulations that zoom in on individual galaxies include more physics than cosmological simulations (Hummels et al. 2013; Su et al. 2018). They qualitatively agree that enhanced star formation feedback increases the strength of high-ionization absorption lines, as Heckman et al. (2017) observed. Quantitatively, however, the star formation feedback does not produce enough O vi absorption, nor does it permanently quench star formation. A solution may require a completely different schema for the CGM. Stern et al. (2018) argue, for example, that the O vi absorption occurs beyond the accretion shock, where O vi would be photoionized by the UV background, a low-pressure scenario.

2020AandA...644A..59K__Pastorello_et_al._2019_Instance_2

The year 2020 marks the 350 yr anniversary of the discovery of the eruption of Nova 1670 (or CK Vul) made by European astronomers (Shara et al. 1985). Their observations, predominantly performed with a naked eye, traced the object’s evolution on the sky in 1670−1672. From the archive records, we know that the eruption was rather unusual, in particular it was very much unlike classical novae. The light curve of CK Vul displayed three peaks and the star was described as reddish in the later stages of the eruption (Hevelius 1671; Shara et al. 1985). These characteristics resemble closest the behavior often observed in (luminous) red novae (Kato 2003; Tylenda et al. 2013), a modern category of eruptive stars known from our and other galaxies (e.g. Pastorello et al. 2019). Red novae are recognized as manifestations of on-going mergers of non-compact stars such as main-sequence dwarfs, sub-giants, or red giants (Soker & Tylenda 2003; Tylenda & Soker 2006; Tylenda et al. 2011; Pastorello et al. 2019). While the number of known red novae, mainly extragalactic ones, is quickly rising (e.g., Stritzinger et al. 2020), we know only a few red-nova remnants that are decades old (Kamiński et al. 2018a). The remnant of the 1670 eruption of CK Vul, as a candidate post-merger site, could be the oldest (counting from the onset of the eruption) known object of this type and as such offers the opportunity to investigate a merger aftermath centuries after the stellar coalescence. The nature of the progenitor system of CK Vul has been debated. Eyres et al. (2018) proposed that the seventeenth-century merger took place between a white dwarf and a brown dwarf, but there is little quantitative evidence to support this. Based on the analysis of the source’s chemical and isotopic composition, including the unique presence of the radioactive isotope of 26Al, Kamiński et al. (2018b) found that the progenitor system of CK Vul included at least one red-giant-branch (RGB) star with a fully developed helium core.

2022MNRAS.512..186K__Chakraborty_et_al._2019a_Instance_1

A widely used statistical property of the sky brightness distribution is its power spectrum (Lazarian 1995; Bharadwaj & Sethi 2001, and others). As the redshifted 21-cm signal is expected to be faint and hard to detect with imaging, estimating its power spectrum or equivalently intensity mapping gives a possible probe of the evolution of the baryonic matter distribution over cosmic time. Bharadwaj & Sethi (2001) show that visibility correlation directly measures the power spectrum. This method and its variants (Datta, Choudhury & Bharadwaj 2007; Choudhuri et al. 2014; Choudhuri et al. 2016; Bharadwaj et al. 2019; Choudhuri et al.2019, and others) have been used to estimate the angular power spectrum of the diffused galactic foreground (Ghosh et al. 2012; Choudhuri et al. 2017b; Chakraborty et al. 2019a; Choudhuri et al. 2020) as well as the power spectrum of H i distribution in nearby galaxies (Dutta et al. 2009; Dutta & Bharadwaj 2013; Nandakumar & Dutta 2020). These works propagate the uncertainties in each visibility estimate and combine that with the sample variance error in measuring the power spectrum to quote uncertainties in the power spectrum estimates. In this work, we use the estimator discussed in Choudhuri et al. (2014), where visibilities are gridded before estimating the power spectrum. Given an angular field of view of θ0 to which the telescope is sensitive, it has been shown (Bharadwaj & Sethi 2001; Bharadwaj & Ali 2005; Choudhuri et al. 2014) that the visibilities in the nearby baselines remain correlated to a baseline separation of $\Delta U \lt \frac{1}{\pi \theta _0}$. The size of the uv-grids is chosen such that they are large enough to include a sufficient number of baselines in a given uv-grid and small enough to have all visibilities in the uv-grid correlated. In each uv-grid, they estimate the power spectrum by correlating visibilities only in nearby baselines, omitting the visibility autocorrelations. This drastically reduces the noise bias in estimates of the power spectrum in uv-grids. The contribution from each uv-grid within a given annulus in $U = \mid \vec{U} \mid$ is then combined, and the real part of it is used to quote the value of the isotropic power spectrum for the baseline separation U. We may schematically write it as (4)$$\begin{eqnarray} \mathcal {E} \lbrace P(U)\rbrace = \mathcal {R} [\langle \tilde{V}(\vec{U})^{*} \tilde{V}(\vec{U}+\Delta \vec{U}) \rangle]. \end{eqnarray}$$Here, the average is taken over the uv-grid first and then within the annulus, as explained above. Note that the power spectrum estimator here assumes that a perfect calibration is done and the gains are all unity. In such a case, the power spectrum estimate has no bias arising from instrumental noise, and its uncertainties can be written as (Ali et al. 2008; Dutta 2011) (5)$$\begin{eqnarray} \sigma _P^2 = \frac{P^2(U)}{N_\mathrm{ G}} + 2\frac{P(U)\sigma _N^2}{N_\mathrm{ B}} + 2\frac{\sigma _N^4}{N_\mathrm{ B}}, \end{eqnarray}$$where NG is the number of independent estimates of the power spectrum in a given annulus bin at U, NB is the total number of visibility pairs in the bin.

2021ApJ...912..132K__Abramowicz_&_Zurek_1981_Instance_1

The magnetic field assumed in our models is normalized either to β = 100, which means thermally dominated accretion flows, or to β = 1, which means actually quite strong magnetization and equilibrium ratio of the magnetic and gas pressures. The gas pressure is set by the solution of the Bondi transonic accretion flow, which is our initial condition for the simulation. Only if the magnetic pressure does not add a high contribution to this flow structure can we safely assume that the solution holds, and the gas falls supersonically to the black hole downstream of the sonic point, ultimately reaching the speed of light at the black hole horizon. We perturb this flow in time, by adding a slow rotation to the gas; hence, our sonic point is moving, and the shape of the sonic surface is disturbed. However, when the magnetic pressure is substantially large, the initial solution for the transonic accretion may not be a good approximation. Already at the beginning, multiple critical points might exist in the flow. The possible existence of shocks in low angular momentum flows connected with the presence of multiple critical points in the phase space has been studied from different points of view during the past 30 yr. However, the theoretical works that describe the fundamental properties of the low angular momentum accretion and that usually treat the steady solution of the equations have so far been carried out only for nonmagnetized and usually also nonviscous flows (Abramowicz & Zurek 1981; Abramowicz & Chakrabarti 1990; Das 2002; Das & Czerny 2012). Hydrodynamical models of the low angular momentum accretion flows have been studied already in two and three dimensions, e.g., by Proga & Begelman (2003) and Janiuk et al. (2008, 2009). In those simulations, a single, constant value of the specific angular momentum was assumed, while the variability of the flows occurred owing to, e.g., nonspherical or nonaxisymmetric distribution of the matter. The level of this variability was also dependent on the adiabatic index (see also Palit et al. 2019). However, these studies considered nonmagnetized flow only, which limits the applicability of these models to observational data and to the stellar evolution and presupernova studies (Heger et al. 2005). The range of strength of the magnetic field varies from very low values in the interstellar medium to extremely high values of ∼ 1015 G in the magnetars, which are extremely magnetized neutron stars. During the accretion process, the magnetic field in the star is likely to be amplified in the innermost region of the black hole, so a negligible amount of magnetic field in those regions, where the shock and the sonic point could be located, is thus not anticipated. The only method, however, to describe the effect of strong magnetic fields in the context of transonic accretion with low angular momentum is via numerical simulations, because no analytic solution of this problem exists, to the best of our knowledge. Hence, setting up the initial condition with strong magnetic fields overimposed on the slowly rotating transonic Bondi flow is a simple but working approach that we make here.

2021AandA...655A..12T__Tang_et_al._2017b_Instance_7

Using the RADEX3 non local thermodynamic equilibrium (LTE) modeling program (van der Tak et al. 2007) with collisional rate coefficients from Wiesenfeld & Faure (2013), we modeled the relation between the gas kinetic temperature and the measured average of para-H2CO 0.5 × [(322–221 + 321–220)/303–202] ratios, adopting a 2.73 K background temperature, an average observational linewidth of 4.0 km s−1, and column densities N(para-H2CO) = 2.7 × 1012 and 3.7 × 1012 cm−2 for N113 and N159W, respectively. The results are shown in Fig. 5. The values of the para-H2CO column density were obtained with APEX data (beam size ~30″; Tang et al. 2017b), which cover similar regions. Different column densities of para-H2CO only weakly affect derived kinetic temperatures (see Fig. 3 in Tang et al. 2017b or Fig. 4 in Tang et al. 2018a; this was also shown in Fig. 13 and discussed in Sect. 4.3.1 of Mangum & Wootten 1993) as long as all lines are optically thin. Considering that the relation between the gas temperature and the para-H2CO line ratio may vary at different spatial densities (see Fig. 2 in Tang et al. 2017b), we modeled it at spatial densities 104, 105, and 106 cm−3 in Fig. 5. It appears that Tkin at n(H2) = 105 cm−3 is consistently lower than values at 104 and 106 cm−3 by ≲23% and ≲34%, respectively, for Tkin ≲ 100 K. Local thermodynamic equilibrium (LTE) is a good approximation for the H2CO level populations under optically thin and high-density conditions (Mangum & Wootten 1993; Tang et al. 2017a,b, 2018b). Following the method applied by Tang et al. (2017b) in their Eq. (2), we plot the relation between the LTE kinetic temperature, TLTE, and the para-H2CO (3–2) line ratio in Fig. 5. Apparently, TLTE agrees well with Tnon-LTE at volume densities n(H2) ~ 105 cm−3 as long as Tkin ≲ 100 K. Previous observations show that para-H2CO (3–2) is sensitive to gas temperature at density 105 cm−3 (Ginsburg et al. 2016; Immer et al. 2016; Tang et al. 2017b). The spatial density measured with para-H2CO (303–202) and C18O (2–1) in N113 and N159W is n(H2) ~ 105 cm−3 on a size of ~30″ (Tang et al. 2017b). Therefore, here we adopt 105 cm−3 as an averaged spatial gas density in the N113 and N159W regions.

2015ApJ...810..107M__LBGs_and_Ouchi_et_al._2008_Instance_1

Here, we present a revised calculation of the emissivity of ionizing photons at z = 2.85 based on the analysis of the HST data in the HS1549 field. We estimate the comoving specific emissivity as 3 following the assumptions of M13 and Nestor et al. (2013). In this expression, L is the non-ionizing UV luminosity, Φ is the non-ionizing UV luminosity function, and (FUV/FLyC)corr is the average flux-density ratio of non-ionizing to ionizing UV radiation for the entire galaxy sample, corrected for the mean IGM attenuation in the LyC spectral region.19 19 To correct for absorption of LyC photons by neutral hydrogen in the IGM, we use the sample-averaged transmission values calculated in M13: tLAE = 0.44 ± 0.03 and tLBG = 0.35 ± 0.04. We perform this emissivity calculation separately for the main sample of spectroscopically confirmed LBGs and LAEs from M13 (using the UV luminosity functions from Reddy et al. 2008 for LBGs and Ouchi et al. 2008 for LAEs), and combine the LBG and LAE emissivities to obtain a total emissivity for star-forming galaxies. As in M13, we use the LRIS V-band to represent non-ionizing UV flux and NB3420 to represent LyC flux. The difference between our calculation and that of M13 lies in our estimation of the average flux-density ratio. Rather than estimating the average amount of foreground contamination from simulations, we instead know exactly which galaxies are contaminated based on the HST data. There were only two NB3420-detected galaxies in the M13 spectroscopic sample for which we were unable to acquire U336V606J125H160 imaging (D24 and lae4680), and for these objects we could not evaluate whether or not the NB3420 detections are due to foreground contamination. We thus calculate the emissivity twice in order to quote the full range of possible values: in one calculation we assume that MD5 is the only true LyC detection, and in the other calculation we assume that MD5, D24, and lae4680 are all true LyC-emitters. In addition to using the HST data to remove the NB3420 flux of foreground contaminants, we also use these measurements to estimate the percentage of contaminated flux in the non-ionizing UV. All objects with foreground contaminants identified through the HST imaging are blended in the LRIS V imaging, and it is impossible to isolate the uncontaminated z ∼ 2.85 flux in the LRIS V image. Thus, for each contaminated object, we decrease its LRIS V-band flux to match the fraction of uncontaminated V606 flux in the HST imaging. For objects that do not have HST U336V606J125H160imaging and are undetected in NB3420, we decrease their LRIS V-band flux to match the average fraction of uncontaminated V606 flux in the full sample of HST-imaged galaxies without NB3420 detections (99% for LBGs, 91% for LAEs). Finally, we use the same sample-averaged IGM correction to compute (FUV/FLyC)corr as described in M13, employing statistics of H i absorbers from Rudie et al. (2013). We note that the clustering of Lyman limit systems is not taken into account in these absorber statistics, and thus the true mean IGM transmission may be slightly higher than the values presented in M13 (see, e.g., Prochaska et al. 2014).

2015AandA...584A..32M__Smolčić_et_al._2015b_Instance_1

Ikarashi et al. (2015) discussed that if both the radio and FIR continuum are tracers of star-forming regions, then the z ≳ 3 SMGs are more compact than the lower-redshift SMGs typically observed in radio continuum emission (e.g. Biggs & Ivison 2008). As shown in Fig. 7, our present VLA 3 GHz data do not suggest such a trend, and, as mentioned earlier, there is actually a hint of larger radio sizes at z ~ 2.5–5 compared to lower redshifts. However, the highest-redshift SMG in our sample, AzTEC3 at z ≃ 5.3, shows the most compact size among our sources, consistent with the rest-frame FIR sizes from Ikarashi et al. (2015). We note that Capak et al. (2011) found that AzTEC3 belongs to a spectroscopically confirmed protocluster containing eight galaxies within a 1 arcmin2 area, and therefore the environment might also play a role in the galaxy size evolution (see also Smolčić et al. 2015b). However, it is currently unclear whether the environmental effects in a galaxy overdensity will lead to a more compact or more extended radio-emitting size than field galaxies. On one side, a protocluster environment is expected to show an elevated merger rate (e.g. Hine et al. 2015), and, as discussed above, mergers are expected to pull the galactic magnetic fields to larger spatial scales, and hence lead to a more extended radio synchrotron emission. On the other side, the ram and/or thermal pressures of the intracluster medium could compress the ISM of the galaxy, increase the magnetic field strength, and hence cause an excess in radio emission (consistent with a low IR-radio q parameter of ≲2 for AzTEC3; Miettinen et al., in prep.). The aforementioned pressure forces can drive shock waves into the ISM, and hence accelerate the CR particles (Murphy 2009). Consequently, the cooling time and diffusion length-scale of CR electrons can decrease (see Appendix E), resulting in a compact radio-emitting area. More detailed environmental analysis of SMGs is needed to understand this further.

2022MNRAS.511.1362T__Tanikawa_et_al._2021a_Instance_1

Among the main proposed formation channels for merging binary COs, we find: pairing of primordial BHs (e.g. Carr & Hawking 1974; Bird et al. 2016; Carr et al. 2016; Scelfo et al. 2018; De Luca et al. 2021); isolated binary evolution via common envelope (e.g. Tutukov & Yungelson 1973; Bethe & Brown 1998; Portegies Zwart & Yungelson 1998; Belczynski, Kalogera & Bulik 2002; Belczynski et al. 2008; Dominik et al. 2013; Belczynski et al. 2016; Eldridge & Stanway 2016; Mapelli et al. 2017; Stevenson, Berry & Mandel 2017; Ablimit & Maeda 2018; Klencki et al. 2018; Kruckow et al. 2018; Mapelli & Giacobbo 2018; Eldridge, Stanway & Tang 2019; Mapelli et al. 2019; Neijssel et al. 2019; Spera et al. 2019), via stable mass transfer (e.g. Kinugawa et al. 2014; Inayoshi et al. 2017; van den Heuvel, Portegies Zwart & de Mink 2017; Kinugawa, Nakamura & Nakano 2020; Tanikawa et al. 2021a,b), or via chemically homogeneous mixing (e.g. de Mink & Mandel 2016; Mandel & de Mink 2016; Marchant et al. 2016; du Buisson et al. 2020); dynamical perturbations in the field (Michaely & Perets 2019, 2020); dynamical formation in young star clusters (YSCs, e.g. Banerjee, Baumgardt & Kroupa 2010; Ziosi et al. 2014; Mapelli 2016; Askar et al. 2017; Banerjee 2017; Banerjee 2018; Rastello et al. 2018; Di Carlo et al. 2019, 2020a,b; Kumamoto, Fujii & Tanikawa 2019, 2020; Rastello et al. 2020; Banerjee 2021; Rastello et al. 2021; Trani et al. 2021); globular clusters (GCs, e.g. Portegies Zwart & McMillan 2000; Downing et al. 2010; Tanikawa 2013; Samsing, MacLeod & Ramirez-Ruiz 2014; Rodriguez et al. 2015; Rodriguez, Chatterjee & Rasio 2016; Rodriguez & Antonini 2018; Samsing, Askar & Giersz 2018; Zevin et al. 2019; Antonini & Gieles 2020); nuclear star clusters (NSCs, e.g. Miller & Lauburg 2009; O’Leary, Kocsis & Loeb 2009; Antonini & Perets 2012; Prodan, Antonini & Perets 2015; Antonini & Rasio 2016; Petrovich & Antonini 2017; Arca-Sedda & Gualandris 2018; Gondán et al. 2018; Arca-Sedda & Capuzzo-Dolcetta 2019; Rasskazov & Kocsis 2019; Arca Sedda 2020; Arca Sedda et al. 2020); and AGN discs (e.g. McKernan et al. 2012; Bartos et al. 2017; Stone, Küpper & Ostriker 2017; McKernan et al. 2018; Yang et al. 2019; Tagawa, Haiman & Kocsis 2020).

2020MNRAS.497.2651K__Teuff,_Millar_&_Markwick_2000_Instance_1

As the H ii regions S235 A and S235 C are deeply embedded in the molecular cloud, we cannot use published predictions from standard plane-parallel PDR models without foreground absorption (e.g. Kaufman et al. 1999). In order to understand how the observational view by SOFIA is matched by a ’classical’ model of expanding H ii regions, described analytically and numerically by Spitzer (1978), Elmegreen & Lada (1977), Hosokawa & Inutsuka (2006), Raga et al. (2012), Kirsanova et al. (2009), and Bisbas et al. (2015), we make simulations with the MARION model (Kirsanova et al. 2009; Akimkin et al. 2015). The gas-phase chemical network from Röllig et al. (2007) (mainly based on the UMIST99 ratefile; Le Teuff, Millar & Markwick 2000) allows us to reproduce the C+, CO, and HCO+ abundances in PDRs (see Kirsanova & Wiebe 2019), so we use this network together with the ionization of the atomic species and corresponding recombinations in the H ii regions. The cross-sections for most of photoreactions are taken from the Leiden data base of ‘Photodissociation and photoionization of astrophysically relevant molecules’ (Heays, Bosman & van Dishoeck 2017). We implement the formation of H2 on grain surfaces and accretion and desorption processes of other neutral species, but other chemical reactions on dust surfaces are not considered in the calculations to save computation time. The rates of accretion and desorption processes are based on the work by Hasegawa & Herbst (1993), with updated desorption energies from Garrod, Wakelam & Herbst (2007). We use the ‘high-metallicity’ initial elemental abundances based on the ‘EA2’ set from Wakelam & Herbst (2008). The initial conditions are cold, molecular, and solid. This means that we start with all carbon and oxygen in CO and H2O on dust surfaces. Fifty two chemical species are included: H, H+, H2, H$_2^+$, H$_3^+$, O, O+, O++, OH+, OH, O2, O2:d, O$_2^+$, H2O, H2O+, H3O+, C, C+, C++, CH, CH+, CH2, CH$_2^+$, CH3, CH$_3^+$, CH4, CH$_4^+$, CH$_5^+$, CO, CO:d, CO+, HCO+, He, He+, S, S+, S++, Si, Si+, H:d, H2:d, O:d, OH:d, H2O:d, C:d, CH:d, CH2:d, CH3:d, CH4:d, S:d, Si:d, and e−, where the postfix ‘:d’ indicates species on dust grain ices. Chemical species containing Si and S are included only to obtain correct gas temperature in ionized region. We recognize that the chemical network is far from complete, but our choise was motivated by limited computation time. The heating and cooling processes included in the model are listed in Akimkin et al. (2015) and Kirsanova & Wiebe (2019).

2017ApJ...850...75S__Bugaev_et_al._2016_Instance_1

A realistic EoS that is able to reproduce the properties of compact astrophysical objects has to fulfill several requirements. The possibility of including many particle species, which is known as multicomponent character, is of crucial importance for modeling the NS interiors, which in even the simplest treatment include neutrons, protons, and electrons, while more advanced descriptions have to account for the presence of hyperons (Schaffner-Bielich et al. 2002). Therefore, the grand canonical ensemble is the natural choice for the formulation of such an EoS. Another element of the realistic phenomenological hadronic EoS corresponds to the short-range repulsive interaction of the hard core nature between particles (Andronic et al. 2006; Bugaev et al. 2016). Analysis of the particle yields produced in relativistic A+A collisions within statistical (thermal) models, i.e., the Hadron Resonance Gas (HRG) model (Bugaev et al. 2016), shows the importance of the particle hard core repulsion. In this approach every particle species is defined as a rigid sphere with a fixed radius estimated from experimental data analysis. These radii do not exceed 0.5 fm (Andronic et al. 2017; Sagun et al. 2017a). Note that the hard core of hadrons in phenomenological EoSs is important in order to suppress thermal excitations of the hadronic spectrum and provide deconfinement of the color degrees of freedom expected at high temperatures/densities (Satz 2012). Another requirement to the phenomenological EoS is related to its causal behavior when the speed of sound cannot exceed the speed of light. At sufficiently high densities this condition is violated by the hard core repulsion. As was shown by Sagun et al. (2017a), introducing the induced surface tension (IST) of particles to the model with the hard core repulsion between an arbitrary number of hadron species makes the EoS significantly softer and extends its causality range up to 7.5 normal nuclear densities, where formation of the quark-gluon plasma is expected. The IST is the key element of this approach (Sagun et al. 2014), as it allows us to account for the hard core repulsion between constituents in the most accurate way, and to properly reproduce the virial expansion of the multicomponent EoS. Recently, the IST EoS was used to describe the experimental data of hadron multiplicities measured at AGS, SPS, RHIC, and LHC energies of nuclear collisions (Sagun et al. 2017b), as well as the nuclear matter properties (Sagun et al. 2014). In this work the focus is on the application of IST EoS to the study of NS properties.

2022ApJ...935..136O__Ioppolo_et_al._2011_Instance_1

HCOOH is the simplest carboxylic acid and has been observed toward high-mass and low-mass star-forming regions (e.g., Woods et al. 1983; Liu et al. 2001, 2002; Bisschop et al. 2007; Lefloch et al. 2017; Oya et al. 2017; Csengeri et al. 2019), protoplanetary disks (e.g., Favre et al. 2018), pre-stellar sources (e.g., Irvine et al. 1990; Vastel et al. 2014; Jiménez-Serra et al. 2016), and comets of the solar system (Biver et al. 2014). Although the production of HCOOH has also been investigated experimentally (Ioppolo et al. 2011) and theoretically (Tielens & Hagen 1982; Garrod & Herbst 2006; Aikawa et al. 2008; Garrod et al. 2008; Vasyunin et al. 2017), its formation process and chemical link to nitrogen-bearing species are puzzling. Indeed, the chemical network calculation tracing a pre-stellar/protostellar core by Aikawa et al. (2020) indicates that nitrogen-bearing COMs and HCOOH appear in the same region (i.e., a hot core) as oxygen-bearing COMs: no differentiation is found. It should be noted that the differentiation is not ascribed to the desorption temperature of these molecules formed on grain mantles. As described in Section 4.2, the temperature of the outer envelope traced by CH3OH is 150–165 K, which is higher than that of the inner envelope traced by HCOOH and NH2CHO, 75–112 K. Hence, ice mantles containing various COMs should have already been liberated from dust grains in the outer envelope. However, the results of PCA-3D suggest that HCOOH and the nitrogen-bearing species, NH2CHO, HNCO, and HC3N, do not appear in the gas phase outside the radius of ∼0.″06, even though these desorption temperatures are comparable to those of CH3OH (e.g., Oya et al. 2019). In other words, other factors rather than the desorption temperature should be responsible for these molecules to be observed in the gas phase. It may be the high-density condition, protostellar radiation, or both of them. The gas-phase production of nitrogen-bearing COMs and HCOOH should be considered seriously.

2016MNRAS.457.3492H__Boylan-Kolchin_et_al._2009_Instance_1

N-body simulations represent the most widely used and convenient method of exploring the highly non-linear regime of cosmic structure formation. Starting from a set of initial conditions, the numerical simulations follow the formation and evolution of structures from an early epoch down to present day. Motivated by the fact that DM represents most of the matter in the Universe and because of the relatively simple physics of collisionless DM particles, DM-only simulations represent the most widely used category of numerical simulations. When designing a cosmological N-body experiment, one is concerned by two major factors. Ideally, one would like to simulate a region of the universe that is as large as possible to get a representative census of the structures encompassed within it. On the other hand, one would also want very high mass resolution, to be able to resolve accurately even the smallest cosmologically relevant objects. Unfortunately, due to limited computational resources, these two requirements are in conflict, which implies that various compromises need to be made when designing a numerical simulation. So far, the biggest efforts were focused into two, somewhat complementary approaches. The first is represented by simulations like Millennium (ms; Springel et al. 2005), Millennium II (ms-II; Boylan-Kolchin et al. 2009), Millennium XXL (MXXL; Angulo et al. 2012), Bolshoi (Klypin et al. 2011), MultiDark (Prada et al. 2012), Horizon Run I-III (Kim et al. 2009, 2011), Horizon-4π (Prunet et al. 2008; Teyssier et al. 2009), MareNostrum Universe (Gottloeber et al. 2006), Jubilee project (Watson et al. 2014), Coyote Universe (Heitmann et al. 2010), DEUS simulation (Alimi et al. 2012; Rasera et al. 2014) or MICE suite (Fosalba et al. 2015). These follow structure formation in a large cosmological volume at the expense of having a medium or a low-mass resolution. Such simulations provide the formation histories for a very large number of medium- and high-mass DM haloes, but do not necessary resolve all the details relevant for galaxy formation. On the other side we have N-body simulations like the aquarius project (Springel et al. 2008), the Via Lactea (Diemand, Kuhlen & Madau 2007), the Phoenix project (Gao et al. 2012), CLUES (Gottloeber, Hoffman & Yepes 2010) and the ELVIS suite (Garrison-Kimmel et al. 2014b) that are characterized by a very high mass and force resolution but are limited to very small cosmic volumes. These give a very detailed picture of galaxy- and cluster-size haloes, but do so only for a very limited number of objects, which makes their results sensitive to small number statistics, and are unable to capture the full interconnection between small (DM haloes) and large (the cosmic web) cosmic scales.

2019AandA...632A..76N__Ni_2018_Instance_1

The Juno spacecraft has measured Jupiter’s gravitational field to high precision through precise Doppler tracking in its polar orbit around Jupiter, compared with the previous values detected by Pioneer 10 and 11 and by Voyager 1 and 2 (Folkner et al. 2017; Bolton et al. 2017; Iess et al. 2018). These new gravity data have improved our knowledge of Jupiter’s interior. The even gravity harmonics are affected by the shape and internal structure of Jupiter in its hydrostatic equilibrium under the effect of rotational distortion. To accurately describe the shape and internal structure of Jupiter, various interior models with new ingredients, such as two-layer and three-layer structure models, have been established by several groups (Guillot & Morel 1995; Guillot 1999; Hubbard 1999, 2013; Anderson & Schubert 2007; Kaspi et al. 2010; Nettelmann et al. 2012; Helled et al. 2015; Vazan et al. 2015; Kong et al. 2016; Wahl et al. 2017; Ni 2018; Guillot et al. 2018; Debras & Chabrier 2019). Most of these models describe the internal structure of Jupiter via the physical laws of nature given an empirical or simulated equation of state (EOS), such as polytropic EOSs, EOSs obtained using the free-energy minimization method (Saumon et al. 1995), and ab initio EOSs (Nettelmann et al. 2008, 2012; French et al. 2012; Becker et al. 2014; Militzer et al. 2008; Militzer & Hubbard 2013; Chabrier et al. 2019). The EOSs describe the microscopic properties of planetary matter and play an important role in calculating the structure and evolution of planets, in spite of uncertainties in the hydrogen–helium phase separation (Hubbard et al. 2002; Saumon & Guillot 2004; Fortney & Nettelmann 2010; Miguel et al. 2016, 2018; Militzer et al. 2016; Debras & Chabrier 2019). Juno’s gravity measurements have demonstrated small values for high-order even gravitational harmonics with respect to the heavy-element abundance measured by the Galileo entry probe (Wahl et al. 2017; Debras & Chabrier 2019). In order to reconcile the calculated gravitational harmonics with these small values of J4 to J8, Wahl et al. (2017) proposed a dilute core model where heavy elements are dissolved in a hydrogen–helium mixture and expanded outward through a portion of Jupiter. Alternatively, these latter authors modified the abundances of helium and heavy elements in the outer molecular envelope to be lower than those measured by the Galileo entry probe. Stimulated by dilute cores, the three-layer structure models for Jupiter have been generalized into four-layer structure models by introducing a dilute core region above central compact cores. Guillot et al. (2018) compared the effective even harmonics $J_{2i}^{\mathrm{eff}}(H,m)\;{=}\;J_{2i}^{\mathrm{Juno}}-\bigtriangleup J_{2i}(H,m)$ J2ieff(H,m) = J2iJuno−△J2i(H,m) with the ones obtained from interior models assuming rigid rotation to explore the rotation of Jupiter’s deep interior. It is found that Jupiter’s deep interior exhibits an almost rigid-body rotation and Jupiter’s atmospheric zonal flow extends to a depth H of 2000−3500 km. Debras & Chabrier (2019) established new models of Jupiter which satisfy both Juno’s gravity data and the outer helium and heavy element abundances from Galileo, showing a considerable entropy increase between the outer and inner envelopes and an inward decreasing abundance of heavy elements in the inner envelope.

2018MNRAS.478...95K__Caselli_et_al._2002_Instance_1

Strong controversies are however still present regarding the way stars form in gravitationally unstable cores, in particular high-mass stars. Whether it happens on approximately a free-fall time, as suggested in the competitive accretion scenario by Bonnell et al. (2001), or rather slowly, implying at least several free-fall times, in the core accretion model by McKee & Tan (2003), which assumes that turbulence and/or magnetic fields provide a major stabilizing contribution. It is therefore of central importance to obtain independent constraints on the time-scale of star formation. A potentially powerful tool that was suggested in the literature is the measurement of deuteration fractions, which may be translated into time-scales via chemical models (Caselli et al. 2002; Fontani et al. 2011; Pagani et al. 2011a; Kong et al. 2015; Barnes et al. 2016; Lackington et al. 2016). In relation to filaments, a time-scale estimate has recently been achieved by Lackington et al. (2016) for the infrared dark cloud L332. They found deuteration ratios N2D+/N2H+ in the range 0.003–0.14. Based on the chemical models by Kong et al. (2015), the authors deduced time-scales for various cores within L332 of the order of several free-fall times to match the observed deuteration fractions, indicating rather old cores. The free-fall time of their best-fitting model was tff = 1.39 × 105 yr at a density of nH = 105 cm−3. However, the authors emphasize that a change in the CO depletion factor reduces the time-scale to only one free-fall time, which points towards (dynamically) young objects. In addition, Barnes et al. (2016) deduce a chemical age of about eight free-fall times (defined for spherical systems at a density of n = 104 cm−3, giving tff = 3.4 × 105 yr) for the dark cloud G035.39-00.33 to fit the observed average deuteration fraction of 0.04 ± 0.01, which they attribute to support of the filament by magnetic fields and turbulence. Furthermore, these authors find that deuteration is widespread over the filament rather than concentrated in individual cores, in agreement with recent findings by Pillai et al. (2012) who report widespread H2D+ emission in Cygnus X.

2016AandA...588A..44Y__Jones_et_al._2014_Instance_1

As described in Köhler et al. (2015)2, we assume that the dust properties change with increasing local density through accretion and coagulation. First, in the transition between the diffuse ISM and dense clouds, a second mantle can form on the surface of the CM grains that is due to the coagulation of the small aromatic-rich carbon grains on top of the bigger grains, which might be subsequently hydrogenated (a-C → a-C:H) and/or to the accretion of a-C:H material from the gas phase. Such carbonaceous mantles are efficiently processed by UV photons (Alata et al. 2014), but can stay H-rich as long as the radiation field is attenuated (Jones et al. 2014) or if the rehydrogenation process is efficient enough. This leads to grains with two mantles (core/mantle/mantle grains, CMM). Second, inside dense clouds, the CMM grains can coagulate into aggregates (AMM). On average, regarding the material abundance, one aggregate is composed of three CMM grains with amorphous silicate cores and one CMM grain with an amorphous carbon core. The formation of ice mantles on the surface of the aggregates (AMMI) can also occur in the densest regions, where the shielding from energetic photons is efficient enough to allow either gas molecules to form and to freeze out on the grains or surface chemistry to proceed effectively. The three types of evolved grains (CMM, AMM, and AMMI) contain 406 ppm of C in agreement with Parvathi et al. (2012), who found that 355 ± 64 ppm of C are enclosed within grains for lines of sights where NH ≳ 2 × 1021 H/cm2 (equivalently where E(B − V) ≳ 0.4 or AV ≳ 2). For each grain type, a size distribution consistent with grain growth is considered as explained in Köhler et al. (2015) and shown in Fig. 2. For the CMM grains, ~65% of the dust mass is in grains with \hbox{$40~{\rm nm} \leqslant a \leqslant 0.25$}40 nm ⩽ a⩽ 0.25μm, ~25% in grains with \hbox{$0.25 \leqslant a \leqslant 0.5$}0.25 ⩽ a⩽ 0.5μm, and ~10% in grains with \hbox{$0.5 \leqslant a \leqslant 0.7$}0.5 ⩽ a⩽ 0.7μm. For the AMM (AMMI) aggregates, ~50% of the dust mass is in grains with \hbox{$48~(91)~{\rm nm} \leqslant a \leqslant 0.25$}48 (91) nm ⩽ a⩽ 0.25μm, ~40% in grains with \hbox{$0.25 \leqslant a \leqslant 0.5$}0.25 ⩽ a⩽ 0.5μm, and ~10% in grains with \hbox{$0.5 \leqslant a \leqslant 0.7$}0.5 ⩽ a⩽ 0.7μm. The optical properties of all grains and a detailed description of the calculation method can also be found in Köhler et al. (2015), while a more specific description of the grain scattering properties and efficiencies are given in Paper I. A schematic view of the dust composition and stratification from the diffuse ISM to dense molecular clouds is shown in Fig. 1.

2020AandA...644A..59K__Heays_et_al._2017_Instance_2

Analyzing optical emission lines, emanating from within the northern lobe, Tylenda et al. (2019) found a reddening with EB − V ≈ 0.9 mag or AV ≈ 2.8 mag, which we assume is mainly circumstellar in origin. Hajduk et al. (2013) observed two stars shining through the southern lobe and found AV = 3.3 − 4.4 mag with unknown contribution from the interstellar component. We assume here that those observations quantify the amount of circumstellar dust that is the main actor in shielding molecules from the central source. We recalculated the lifetimes of molecules assuming AV = 3 mag, and with (1) standard dust properties (i.e. with composition and size distribution as of interstellar dust) or (2) with larger and less opaque grains, at the gas-to-dust mass ratio of 124 (see Heays et al. 2017, for more details on the assumed dust properties). We used shielding functions from Heays et al., which include effects in lines. Results are shown in Cols. (3) and (4) of Table 3. The presence of ISM grains makes it possible for the observed molecules to survive for a very long time, longer than 350 yr. The lifetimes in the presence of the large grains considered by Heays et al. are typically a few times shorter than the age of the remnant. It is uncertain what kind of grains populate the dusty remnant of CK Vul, but given its anomalous elemental and molecular compositions and eruptive history, dust may have a peculiar chemical composition and size distribution. In such a case, the total to selective extinction law would also be different and the assumed AV may not be adequate. Nevertheless, if the molecules formed 350 yr ago and are shielded by big grains, with the calculated lifetimes a considerable fraction of molecular species would survive, except perhaps for a few most fragile ones which indeed are almost absent in the lobes. We conclude that the lifetimes in Table 3 that were calculated with an attenuated ISRF are consistent with the molecule formation 350 yr ago or more recently.

2015AandA...576A...5C__Jørgensen_et_al._2012_Instance_2

The relative abundances of the three species are derived from the column densities in Table 2 and are compared with other star-forming regions and comets in Table 3. The (CH2OH)2/CH2OHCHO abundance ratio of ~0.3–0.5 previously derived in IRAS 16293 by Jørgensen et al. (2012) was revised. Indeed, the assignment in Jørgensen et al. (2012) was based on only one line of the gGg′ conformer of ethylene glycol about 200 cm-1 (~290 K, Müller & Christen 2004) above the lowest-energy aGg′ conformer – and thus tentative. An analysis from observations of six transitions of the lower energy conformer from ALMA Cycle 1 observations at 3 mm (four spectral windows at 89.48–89.73, 92.77–93.03, 102.48–102.73, and 103.18–103.42 GHz; Jørgensen et al., in prep.) results in a higher ethylene glycol-to-glycolaldehyde abundance ratio of 1.0 ± 0.3. This new estimate is consistent with the ratio expected between the aGg′ and gGg′ conformers under thermal equilibrium conditions at 300 K, the excitation temperature of glycolaldehyde derived in IRAS 16293 (Jørgensen et al. 2012). The (CH2OH)2/CH2OHCHO abundance ratio in IRAS2A is estimated at 5.5 ± 1.0 if we consider the column densities derived from the rotational diagrams. It is slightly lower (4.6), however, if we use the column density of ethylene glycol of 1.1 × 1016 cm-2 that does not overproduce the peak intensities of a few lines (see Fig. 3). The (CH2OH)2/CH2OHCHO abundance ratio consequently is a factor ~5 higher than in the Class 0 protostar IRAS 16293. It is also higher than in the other star-forming regions (see Table 3), but similar to the lower limits derived in comets (≳3–6). This indicates that the glycolaldehyde chemistry may in general vary among hot corinos. It is possible that like IRAS2A, other very young low-mass protostars show high (CH2OH)2/CH2OHCHO abundance ratios, in agreement with the cometary values. The CH3OCHO/CH2OHCHO column density ratio found in IRAS2A (~20) ranges between the values derived in the molecular clouds from the Galactic center (~3.3–5.2) and the high-mass star-forming regions (~40–52). A lower limit of 2 was derived for comet Hale-Bopp.

2021ApJ...914...52F__Dere_et_al._1997_Instance_1

In Equations (24) and (25) the cumulative effect of all ions with Zi ≥ 2 is included in the term ζ(T, f), where a (rather weak) dependence on frequency f appears due to the Gaunt factor. The ζ(T, f) function depends both on the elemental abundances and on each element’s ionization state, which are different in equilibrium and nonequilibrium conditions. In this work, we will provide the ζ(T, f) function calculated under the assumption of ionization equilibrium using the CHIANTI spectral code (Dere et al. 1997, 2019), as detailed below. In this framework it is straightforward to users to replace the CHIANTI values for the equilibrium ion fractions by their own values calculated under nonequilibrium conditions, depending on the physical process they model. We carried out the calculations for the plasma element abundances typical of the solar corona (Feldman 1992), as well as for two abundance models obtained for the solar photosphere (Caffau et al. 2011; Scott et al. 2015). The main difference between these models is that in the solar corona all elements with low first ionization potential (FIP) are overabundant by a factor of about four over their photospheric abundances. However, this overabundance factor is somewhat uncertain—while it has been extensively used in the field, its value has been suggested to vary with time (Widing & Feldman 2001) and also within the same active region; also, the absolute correction of the abundances has been disputed, as some studies suggest that the FIP effect affects both low-FIP and high-FIP ions (Schmelz et al. 2012). Furthermore, solar abundances can be different from those of other stars, both in the photosphere and in the corona, even to the point that some stars exhibit an inverse FIP effect (Laming 2015). So, the software we implemented to carry out the calculations allows the user to interactively select an abundance data set that is best suited for the star or the solar region under consideration.

2020ApJ...903L..28S__Shivaei_et_al._2015_Instance_1

The effectiveness of an IRX-β relation depends on its validity for different types of galaxies across cosmic time. While the original MHC99 IRX-β relation applies to majority of galaxies, large scatters around this relation have been observed. Theoretical studies have shown that the IRX-β scatter may depend on the type of dust, gas metallicity, star formation history (SFH), dust-star geometry, and stellar population age (Popping et al. 2017; Safarzadeh et al. 2017; Narayanan et al. 2018; Schulz et al. 2020). Observations have shown that the IRX-β relation varies with stellar mass (Bouwens et al. 2016, 2020; Reddy et al. 2018; Fudamoto et al. 2020), infrared (IR) luminosity (Buat et al. 2012; Casey et al. 2014), age (Siana et al. 2009; Reddy et al. 2010; Shivaei et al. 2015), redshift (Capak et al. 2015; Pannella et al. 2015), and intrinsic β0 (β for a dust-free system; Boquien et al. 2012; Reddy et al. 2018; Schulz et al. 2020). These variations are linked to the diversity of galaxies’ attenuation curves, seen in previous studies (e.g., Kriek & Conroy 2013; Scoville et al. 2015; Battisti et al. 2020). The attenuation curve variations stem from two main sources: (a) different geometrical distributions of dust with respect to stars (and different dust optical depths), and (b) different dust grain properties, which affect the shape of the underlying extinction curve irrespective of the dust-star geometry (see the review by Salim & Narayanan 2020). While scenario (a) is extensively studied by comparing the attenuation curves of galaxies with different dust optical depths (references above), scenario (b) is less explored. Dust compositions are related to gas-phase element depletions and abundances (Jenkins 2009). Following this relation, Shivaei et al. (2020, hereafter S20) studied scenario (b) by deriving the attenuation curve of z ∼ 2 galaxies in two different metallicity ( ) bins, while the dust optical depth distributions were kept the same. They found a steep SMC-like curve for galaxies with , and a shallower Calzetti et al. (2000, hereafter C00)-like curve at .

2018MNRAS.479.3254V___2000_Instance_1

The lifetime of molecular clouds (MCs) remains an active research topic in the study of the interstellar medium and star formation, and most recent studies, both observational and theoretical, place this lifetime at a few times 107 yr for clouds in the 105–106M⊙ mass range (e.g. Blitz & Shu 1980; Kawamura et al. 2009; Zamora-Avilés, Vázquez-Semadeni & Colín 2012; Zamora-Avilés & Vázquez-Semadeni 2014; Lee, Miville-Deschênes & Murray 2016). In addition, several observational studies have suggested that the star formation rate (SFR) of the clouds appears to increase over their lifetimes. For example, studies of young clusters embedded in moderate-mass MCs (∼104M⊙) (e.g. Palla & Stahler 1999, 2000; Da Rio et al. 2010) have shown that their age histograms contain a large majority of young (1–2 Myr) objects, but also a tail of older (up to several Myr) ones suggesting an accelerating star formation activity, sometimes followed by a subsequent decline (see also Povich et al. 2016; Schneider et al. 2018). In addition, Kawamura et al. (2009) reported a clear evolutionary process over the lifetime of giant molecular clouds (GMCs; of masses ∼105–106M⊙) in the Large Magellanic Cloud, evidenced by the increasing number of massive stars across the sequence of GMC ‘classes’ proposed by those authors. Finally, on the basis of the large scatter in the observed star formation efficiency (SFE) in Milky Way GMCs, Lee et al. (2016) have concluded that the SFR in those clouds must also be time variable. Numerical simulations of MC formation and evolution also exhibit time varying, increasing SFRs during their early stages (e.g. Vázquez-Semadeni et al. 2007; Hartmann, Ballesteros-Paredes & Heitsch 2012). Also, in the presence of stellar feedback, at late times the SFRs reach a maximum and begin to decrease again (e.g. Vázquez-Semadeni et al. 2010; Colín, Vázquez-Semadeni & Gómez 2013). Vázquez-Semadeni, González-Samaniego & Colín (2017) have recently shown that the simulations of Colín et al. (2013) in fact produce stellar age histograms highly resemblant of the observed ones (Palla & Stahler 1999, 2000; Da Rio et al. 2010), and reproduce observed radial age gradients in clusters (Getman et al. 2014) as well as bottom-heavy stellar initial mass functions (IMFs) in scattered regions of massive star formation (Povich et al. 2016).

2022MNRAS.517.1058H__Navarro-González_et_al._1989_Instance_1

It is likely that some of these products are also formed in our experiments, but except for CO2, HNCO, and NCO−, they cannot be unequivocally identified with IR spectroscopy alone. Some of them have the same functional groups as urea and their IR spectra present absorptions in the same spectral regions, albeit with different band shapes and intensities. They could contribute somehow to the changes, both in shape and intensity, observed in the absorption bands during processing and thus introduce uncertainty, but by analogy with the literature results just mentioned (Renoult et al. 1969; Navarro-González et al. 1989; Duvernay et al. 2005; Poch et al. 2014) we expect that the prevalent dissociation channels will lead to small molecules that will not interfere much with the absorptions of urea. It is worth noting that urea fits in an isoelectronic sequence with carbonic acid and acetone: O = C(OH)2, O = C(NH2)2, and O = C(CH3)2. Under energetic processing, carbonic acid ice has been shown to decompose into H2O and CO2 (Peeters et al. 2010), and acetone ice into CH4 and H2C2O (ketene) among other products (Hudson 2018). In a similar way, urea, the middle term in the sequence, should decompose into NH3 and HNCO. Acid base reactions could transform HNCO and NH3 into OCN− and NH4+. As indicated repeatedly in the text, HNCO and OCN− were found in our measurements, but we did not find NH3 (expected peak at 1070 cm−1, A = 1.4 × 10−17 cm molecule−1). The presence of NH4+, which has been reported by Poch et al (2014) in the photolysis of crystalline urea, is not evident in our measurements, but cannot be completely excluded. The ν4 bending mode of ammonium (A = 4.1 × 10−17 cm molecule−1; van Broekuizen et al. 2004) appears at 1440–1480 cm−1, but its exact location and bandwidth are strongly dependent on the ice environment (see discussion in Maté et al. 2009; Gálvez et al. 2010). In our case, the corresponding ammonium band, which would be the most obvious counterion for OCN−, could be masked by the νCN band of urea. Compounds of very high molecular weight do not seem to form in appreciable amounts in our experiments, since all products evaporate without leaving any residue upon warming of the substrate to 300 K during a few minutes.

2018AandA...617A..86L__Li_et_al._2015b_Instance_1

The IRIS spectra measure the flare in a “sit-and-stare” mode with a roll angle of 45∘. The spectral scale is ∼25.6 mÅ per pixel in the far-ultraviolet (FUV) wavelengths. The IRIS slit crosses the flaring loop and one ribbon (Fig. 1). Two red bars enclose the flaring loop region used to study the quasi-periodic oscillations in this work. IRIS spectrum was pre-processed with the SSW routines of “iris_orbitval_corr_l2.pro” (Tian et al. 2014; Cheng et al. 2015) and “iris_prep_despike.pro” (De Pontieu et al. 2014). To improve the signal-to-noise ratio, we apply a running average over five pixels to the IRIS spectra along the slit (Tian et al. 2012, 2016). We also manually perform the absolute wavelength calibration using a relatively strong neutral line, O I 1355.60 Å (see De Pontieu et al. 2014; Tian et al. 2015; Tian 2017). IRIS observations show that Fe XXI 1354.08 Å is a hot (∼11 MK) and broad emission line and is always blended with many narrow chromospheric lines at the flaring ribbons (Li et al. 2015b, 2016b; Tian et al. 2015; Young et al. 2015; Brosius et al. 2016; Polito et al. 2016). However, the Fe XXI 1354.08 Å line is much stronger than those blended emission lines at the flaring loops (Tian et al. 2016). Figure 2a gives the time evolution of the line profiles of Fe XXI 1354.08 Å, averaged over the slit positions between ∼18.3″ and 21.6″. Figure 2 panels b−f show the spectral line profiles at the time indicated by the yellow lines in panel a. We can see that only the cool line of C I 1354.29 Å is blended with the hot line of Fe XXI 1354.08 Å, but its contribution is negligible. Therefore, double Gaussian functions superimposed on a linear background are used to fit the IRIS spectra at “O  I” window (Tian et al. 2016). Next, we can extract the hot line of Fe XXI 1354.08 Å, as shown by the turquoise profile. The purple profile is the cool line of C I 1354.29 Å. Two orange peaks represent the cool lines of O I 1354.60 Å and C I 1354.84 Å (Tian 2017), which are far away from the flaring line of Fe XXI 1354.08 Å. Finally, the line properties of Fe XXI 1354.08 Å are extracted from the fitting results, that is, Doppler velocity, peak intensity, and line width (Li et al. 2016b; Tian et al. 2016; Tian & Chen 2018).

2018MNRAS.477.1664G__Behroozi,_Wechsler_&_Wu_2013_Instance_1

We test our calibration procedure on the buzzard-v1.1 simulation, a mock DES Y1 survey created from a set of dark-matter-only simulations. The simulation and creation of the mock survey data are detailed in DeRose et al. (in preparation), Wechsler et al. (in preparation), and MacCrann et al. (2018), so we provide only a brief summary of both. buzzard-v1.1 is constructed from a set of three N-body simulations run using l-gadget2, a version of gadget2 (Springel 2005) modified for memory efficiency. The simulation boxes ranged from 1 to 4 Gpc h−1. Light cones from each box were constructed on the fly. Haloes were identified using rockstar (Behroozi, Wechsler & Wu 2013), and galaxies were added to the simulations using the Adding Density Dependent GAlaxies to Light-cone Simulations (addgals) algorithm (Wechsler et al., in preparation). addgals uses the large-scale dark matter density field to place galaxies in the simulation based on the probabilistic relation between density and galaxy magnitude. The latter is calibrated from subhalo abundance matching in high-resolution N-body simulations. SEDs are assigned to the galaxies from a training set of spectroscopic data from SDSS Data Release 7 (DR7; Cooper et al. 2011) based on local environmental density. The SEDs are integrated in the DES pass bands to generate griz magnitudes. Galaxy sizes and ellipticities are drawn from distributions fit to deep SuprimeCam $i^{{\prime }}$-band data. Galaxies are added to the simulation to the projected apparent magnitude limit of DES Year 5 (Y5) data out to redshift z = 2. The galaxy positions, shapes and magnitudes are then lensed using the multiple-plane ray-tracing code Curved-sky grAvitational Lensing for Cosmological Light conE simulatioNS (calclens; Becker 2013). Finally, the catalogue is cut to the DES Y1 footprint with RA > 0 using the footprint and bad region masking including bright stars, regions of high extinction, etc., used in the actual Y1 data, and photometric errors are added using the DES Y1 depth map (Rykoff, Rozo & Keisler 2015). This yields a total masked area of 1108.13 deg2, 12 million WL source galaxies, and 102 120 galaxies in the higher luminosity redMaGiC sample used in this paper, as will be discussed in Sections 3.2 and 3.3.

2016AandA...593A..22R__Shibuya_et_al._2015_Instance_1

Although it is a simple concept, obtaining galaxy sizes is not an easy task and is subject to a number of assumptions. The most common way to derive galaxy sizes is by performing light-profile fitting assuming a given shape of the surface brightness profile using a χ2 minimization (e.g. Simard et al. 1999; Peng et al. 2002; Ravindranath et al. 2004; Daddi et al. 2005; Ravindranath et al. 2006; Trujillo et al. 2006; Akiyama et al. 2008; Franx et al. 2008; Tasca et al. 2009; Cassata et al. 2010, 2013; Williams et al. 2010; Mosleh et al. 2011; Huang et al. 2013; Ono et al. 2013; Stott et al. 2013; Morishita et al. 2014; van der Wel et al. 2014; Straatman et al. 2015; Shibuya et al. 2015). Another method assumes circular or elliptical apertures around a predefined galactic center and computes the size enclosing a given percentage of the total galaxy flux (e.g. Ferguson et al. 2004; Bouwens et al. 2004; Hathi et al. 2008; Oesch et al. 2010; Ichikawa et al. 2012; Curtis-Lake et al. 2016). A third approach, involving counting the number of pixels belonging to the galaxy to derive its size, was also explored in Law et al. (2007). Studies of galaxy sizes at z> 2 became possible with the deep imaging obtained with HST. The first reports on size evolution found that galaxy sizes as observed in the UV rest-frame were becoming smaller at the highest redshifts (Bouwens et al. 2003, 2004; Ferguson et al. 2004). We have now access to the size evolution up to z ~ 10 from the deepest HST imaging data (e.g., Hathi et al. 2008; Jiang et al. 2013; Ono et al. 2013; Kawamata et al. 2015; Holwerda et al. 2015; Shibuya et al. 2015). With the multiwavelength and near-infrared coverage of CANDELS (Grogin et al. 2011; Koekemoer et al. 2011) optical rest-frame measurements are reported up to z ~ 3 for a large collection of galaxies in diverse populations (e.g. Bruce et al. 2012; van der Wel et al. 2014; Morishita et al. 2014). At z ~ 2 the size of star-forming galaxies (SFGs) is, to first order, independent of the observed rest-frame bands (Shibuya et al. 2015). It is generally accepted that galaxy sizes tend to decrease with increasing redshift (e.g. Bouwens et al. 2003, 2004; Ferguson et al. 2004; Mosleh et al. 2012) and that galaxy sizes depend on stellar mass (e.g. Franx et al. 2008; van der Wel et al. 2014; Morishita et al. 2014) and luminosity (e.g. Grazian et al. 2012; Huang et al. 2013). However, some results point to a scenario consistent with no size evolution as seen in UV rest-frame from HST data (Law et al. 2007; Curtis-Lake et al. 2016) and, at a fixed stellar mass, from optical rest-frame ground-based data (Ichikawa et al. 2012; Stott et al. 2013).

2017AandA...602A..75R__Kaneko_&_Yokoyama_(2015)_Instance_1

We can see how this is consistent with the development of small scales via phase mixing by introducing local wavenumbers for the variation with α and β, (3)\begin{eqnarray} \xi \propto \exp {\rm i}\left[ \int \kappa_\alpha {\rm d}\alpha + \int \kappa_\beta {\rm d}\beta \right]\!. \label{number3} \end{eqnarray}ξ∝expi∫καdα+∫κβdβ.Here κα and κβ are the wavenumbers in α and β and have units that are the inverse of the units of their respective coordinates. These wavenumbers should be distinguished from the perpendicular components of the usual wave vector k, which has units of 1/length. The different wavenumbers may be related through the scale factors (h) that relate elemental coordinate increments to physical distances: dr = eαhαdα + eβhβdβ + eγhγdγ, where eα is a unit vector in the α direction, etc. In this notation ∇⊥ = (eα/hα)∂/∂α + (eβ/hβ)∂/∂β, and noting that ∇⊥ξ ≈ ik⊥ξ, Eq. (3) yields \begin{eqnarray} \bdelp\xi &=& {\rm i} \vec{k}_\perp \xi = {\rm i} \left( \frac{\vec{e}_\alpha}{h_\alpha}\frac{ \partial}{\partial\alpha} +\frac{\vec{e}_\beta}{h_ \beta}\frac{ \partial}{\partial \beta}\right)\xi \\ &\equiv& {\rm i}\left( \frac{\vec{e}_\alpha}{h_\alpha}\kappa_\alpha +\frac{\vec{e}_\beta}{h_ \beta}\kappa_\beta\right)\xi. \label{number4} \end{eqnarray}∇⊥ξ=ik⊥ξ=ieαhα∂∂α+eβhβ∂∂βξ≡Equating components of the second and fourth expressions in Eq. (5) gives the expected relations between the various wavenumbers, (6)\begin{eqnarray} k_\alpha = \kappa_\alpha /h_\alpha, \qquad k_\beta = \kappa_ \beta /h_ \beta. \label{number5} \end{eqnarray}kα=κα/hα, kβ=κβ/hβ.Equations (2) and (5) give a direct and elegant expression for the perpendicular wave vector as (7)\begin{eqnarray} \vec{k}_\perp \approx -(\bdel \omega_{\rm c}) t, \label{number6} \end{eqnarray}k⊥≈−(∇ωc)t,which is a generalisation to three dimensions of the results of Mann et al. (1995), (Wright et al. 1999) and Kaneko & Yokoyama (2015) for lower dimensional systems, which developed phase mixing in only one perpendicular coordinate. The above expression allows phase mixing in both perpendicular directions, giving physical phase mixing lengths (or wavelengths) in the α and β directions of (8)\begin{eqnarray} L_{{\rm ph}\alpha} = \frac{2\pi}{|k_\alpha |} \equiv \frac{2\pi h_\alpha}{|\partial \omega_{\rm c}/\partial\alpha |t}, \qquad L_{{\rm ph}\beta} = \frac{2\pi}{|k_ \beta |} \equiv \frac{2\pi h_\beta}{|\partial \omega_{\rm c}/\partial \beta |t}\cdot \label{number7} \end{eqnarray}Lphα=2π|kα|≡2πhα|∂ωc/∂α|t, Lphβ=2π|kβ|≡2πhβ|∂ωc/∂β|t·If the phase mixing lengths are expressed in the same units as α and β, rather than physical length as in Eq. (8), slightly simpler expressions are found, i.e. (9)\begin{eqnarray} \ell_{{\rm ph}\alpha} = \frac{2\pi}{|\kappa_\alpha |} \equiv \frac{2\pi}{|\partial \omega_{\rm c}/\partial\alpha |t}, \qquad \ell_{{\rm ph}\beta} = \frac{2\pi}{|\kappa_ \beta |} \equiv \frac{2\pi}{|\partial \omega_{\rm c}/\partial \beta |t}\cdot \label{number8} \end{eqnarray}ℓphα=2π|κα|≡2π|∂ωc/∂α|t, ℓphβ=2π|κβ|≡2π|∂ωc/∂β|t·The development of the phase mixing length can be pictured simply as the tendency for each field line to oscillate with its own natural frequency. Even if all the field lines start to oscillate with the same phase, they soon drift out of phase with one another as time passes. Not only does the phase mixing process generate perpendicular scales, but points of constant phase can be seen to move across field lines. This phase motion has been seen in magnetospheric data of Alfvén waves (see the review by Wright & Mann 2006) and the simulations of coronal oscillations by Kaneko & Yokoyama (2015). These studies note that the direction of motion is related to the spatial variation of ωc. The results of these papers for the perpendicular phase velocity in physical space generalise to Vph = ωc/k⊥, giving the components (10)\begin{eqnarray} V_{{\rm ph}\alpha} = \frac{-\omega_{\rm c} h_\alpha}{(\partial \omega_{\rm c}/\partial\alpha )t}, \qquad V_{{\rm ph}\beta} = \frac{-\omega_{\rm c} h_ \beta}{(\partial \omega_{\rm c}/\partial \beta )t}, \qquad \label{number9} \end{eqnarray}Vphα=−ωchα(∂ωc/∂α)t, Vphβ=−ωchβ(∂ωc/∂β)t, If the excitation occurred at a time ti, the subsequent properties are found by replacing t with t−ti in the above formulae.

2018MNRAS.480.2881M__Berkley,_Kazanas_&_Ozik_2000_Instance_1

There has additionally been the concern that the optical light curves do not look as expected if they arise from reprocessing of X-ray emission from a small central corona, e.g. of size similar to that which we measure from microlensing observations, i.e. $\rm{\, \stackrel{\lt }{_\sim }\,}10 R_{G}$ (Dai et al. 2010; Mosquera et al. 2013), or from X-ray low/high energy reverberation, i.e. ∼4Rg (Cackett et al. 2014; Emmanoulopoulos et al. 2014). The observed optical light curves are smoother than expected and an insufficient fraction of the X-ray emission hits the disc to power the optical variability (e.g. Berkley, Kazanas & Ozik 2000; Arévalo et al. 2008). Larger coronal sizes are required. Gaskell (2008) also notes the energetics problem and proposes variations originating independently in different parts of the disc. However, although such a model is useful for explaining the uncorrelated variations between bands which are sometimes seen, it cannot explain the well correlated multiwavelength variations seen in the Swift observations. Gardner & Done (2017) proposed an alternative model in which the X-ray emission does not directly impact on the outer disc but mainly heats up the very inner edge of the disc, which then inflates and re-radiates at hard UV wavelengths on to the outer disc. In this model there should be an additional lag between the X-ray and UVW2 emission, over and above that expected from an extrapolation of the longer wavelength lags down to the X-ray waveband. This additional lag would correspond to the thermal time-scale for the incident X-ray heating to pass through the inner disc to the re-radiation surface. Gardner & Done (2017) note the existence of such a lag when the unfiltered X-ray and UVW2 observations of NGC 5548 are compared. However, McHardy et al. (2014) do not see any additional lag in NGC 5548 if those light curves are filtered to remove variations on time-scales longer than 20 d. In NGC 4151, Edelson et al. (2017) see a very large excess lag between the X-ray and UVW2 bands. Unlike in NGC 5548, the excess lag in NGC 4151 is strongly energy dependent, with the highest energy X-rays having the largest lag. NGC 4151 is the most absorbed of the few AGN whose lags have been well studied so far and so the energy dependence may be a function of scattering in the absorbing medium.

2022MNRAS.511.4333K__Gibbons_et_al._2014_Instance_1

The first structures we consider are cosmic voids. Cosmic voids are defined as large underdense regions of the cosmic web, they are the largest structures in the Universe and make up most of its volume (Cautun et al. 2014; Falck & Neyrinck 2015). Historically, their existence was one of the earliest predictions of the concordance cosmological model (Hausman, Olson & Roth 1983), and their observational detection goes back to roughly 40 yr ago (Gregory, Thompson & Tifft 1978; Kirshner et al. 1981). Voids are in particular extremely underdense near their centres, and their spherically averaged density profile shows a characteristic shape (Colberg et al. 2005; Ricciardelli, Quilis & Planelles 2013; Hamaus, Sutter & Wandelt 2014a; Nadathur et al. 2014b; Ricciardelli, Quilis & Varela 2014). Recently, cosmic voids are becoming a promising cosmological probes: first they could represent a population of statistically ideal spheres with a homogeneous distribution at different redshifts which size evolution could be used to probe the expansion of the Universe using Alcock & Paczynski tests (Alcock & Paczynski 1979; Lavaux & Wandelt 2012; Sutter et al. 2012; Sutter et al. 2014b; Hamaus et al. 2015; Hamaus et al. 2016; Mao et al. 2017; Hamaus et al. 2022). Moreover, due to their low density, voids are naturally sensitive to dark energy and thus the interest to use them as probe of alternative Dark Energy models and modified gravity scenarios is increasing (Odrzywołek 2009; Lavaux & Wandelt 2010; D’Amico et al. 2011; Li 2011; Bos et al. 2012; Clampitt, Cai & Li 2013; Gibbons et al. 2014; Barreira et al. 2015; Cai, Padilla & Li 2015; Pisani et al. 2015; Zivick et al. 2015; Pollina et al. 2016; Baldi & Villaescusa-Navarro 2018), as well as the possibility of using them to put constraints on neutrinos masses (Massara et al. 2015; Kreisch et al. 2019; Contarini et al. 2021). Their imprint on the observed Cosmic Microwave Background (CMB) is also becoming an encouraging new cosmological probe, either through their Integrated Sachs-Wolfe (ISW) imprint (Baccigalupi, Amendola & Occhionero 1997; Baccigalupi 1999; Granett, Neyrinck & Szapudi 2008; Cai et al. 2014; Granett, Kovács & Hawken 2015; Hotchkiss et al. 2015; Ade et al. 2016; Nadathur & Crittenden 2016; Kovács et al. 2017; Kovács et al. 2019; Hang et al. 2021), or their lensing imprint (Cai et al. 2017; Raghunathan et al. 2020; Vielzeuf et al. 2021). Furthermore, the observed cold spot of the CMB could be explained as the imprint of the ISW sourced by very large voids along the line of sight (Rees, Sciama & Stobbs 1968; Kovac et al. 2013; Finelli et al. 2014; Nadathur et al. 2014a). Moreover, some works such as Jamieson & Loverde (2019) studied the properties of the voids via the separate universe technique. Finally, some studies tried to link high redshift intergalactic voids in the transmitted Lyman-α flux to the gas density (Viel, Colberg & Kim 2008). Because they are almost empty regions, their evolution during cosmic history is at most weakly non-linear and their properties could possibly be impacted by the primordial density fields from which they formed. This fact motivates us to investigate the effects of baryon-CDM relative perturbations on these objects and their statistics.

2021AandA...656A.137G__White_et_al._1997_Instance_1

To determine the median spectral index and the variation within the sample we perform a bootstrap analysis, randomly drawing from our sample (with replacement) and median stacking both the LoTSS and FIRST cutouts independently to obtain a median peak pixel flux density measurement in each survey. This is then repeated 10 000 times to produce a distribution that also incorporates uncertainties due to sample variation. The 16th and 84th percentile of the resulting peak flux density distributions, together with the systematic flux scale density uncertainty of 5% (White et al. 1997) and 10% (Shimwell et al., in prep.) for the FIRST and LoTSS survey, respectively, are used to determine the error on the spectral index. This stacking procedure is done for all 93 quasars that overlap with FIRST (red) and for only the 35 sources detected by LoTSS with S/N > 2 that overlap with FIRST (grey), resulting in spectral indices of − 0 . 29 − 0.09 + 0.10 $ -0.29^{+0.10}_{-0.09} $ and − 0 . 24 − 0.22 + 0.09 $ -0.24^{+0.09}_{-0.22} $ , respectively. Six of the FIRST detected quasars have spectral indices higher than these stacked values, which is as expected because these flat or positive spectral index quasars are naturally selected because of the FIRST flux limit of ∼144 μJy, which is shallower than the LOFAR flux limit assuming a negative spectral index. In general, LOFAR detected quasars are brighter at 144 MHz than the non-detected ones, therefore one might expect the stacked S/N > 2 sample to result in a steeper spectral index than when stacking all quasars. However, given the uncertainties on the median spectral index there is no significant deviation found between the two. In this work, we therefore assume a spectral index of −0.29±0.10 for the quasars not detected by FIRST when converting radio fluxes to luminosities. This spectral index is in line with the previous work of Gürkan et al. (2019), who found a median spectral index of −0.26 ± 0.02 for a large sample of optically selected quasars at z ≲ 3.

2018MNRAS.473.3810Y__Mitrushchenkov_et_al._2017_Instance_1

The lack of data on inelastic processes due to collisions with neutral hydrogen atoms has been a major limitation on modelling of F-, G- and K-star spectra in statistical equilibrium, and thus to reliably proceeding beyond the assumption of local thermodynamic equilibrium (LTE) in analysis of stellar spectra and the determination of elemental abundances. This problem has been well documented, e.g. see Lambert (1993); Barklem (2016a) and references therein. Significant progress has been made in recent times through detailed full-quantum scattering calculations, based on quantum chemical data, for the cases of simple atoms such as Li, Na, Mg and Ca (Belyaev & Barklem 2003; Barklem, Belyaev & Asplund 2003; Belyaev et al. 2010; Barklem et al. 2010; Belyaev et al. 2012; Barklem et al. 2012; Mitrushchenkov et al. 2017). These calculations have demonstrated the importance of the ionic-covalent curve crossing mechanism leading naturally to charge transfer processes (mutual neutralization and ion-pair production), in addition to excitation and de-excitation processes. The importance of this mechanism has allowed various simplified model approaches to be developed, which may be used in cases where suitable quantum chemistry data are not been available. In particular a semi-empirical model has been employed for Al, Si, Be and Ca (Belyaev 2013a,b; Belyaev, Yakovleva & Barklem 2014b; Yakovleva, Voronov & Belyaev 2016; Belyaev et al. 2016), and a theoretical model based on a two-electron asymptotic linear combinations of atomic orbitals (LCAO) approach, has also been employed for Ca (Barklem 2016b, 2017). Comparisons of the two methods show quite good agreement and reasonable agreement with the full quantum calculations is found, particularly for the most important processes with the largest rates (Barklem 2016b, 2017; Mashonkina, Sitnova & Belyaev 2017; Mitrushchenkov et al. 2017). Thus, the model approaches provide a useful route for obtaining estimates of the rates for these processes for many elements of astrophysical interest.

2019MNRAS.490.1714P__Dolag_et_al._2009_Instance_1

The E-MOSAICS project (Pfeffer et al. 2018; Kruijssen et al. 2019a) is a suite of cosmological, hydrodynamical simulations of galaxy formation in the Λ cold dark matter cosmogony that couples the MOSAICS model for star cluster formation and evolution (Kruijssen et al. 2011; Pfeffer et al. 2018) to the EAGLE model of galaxy formation and evolution (Crain et al. 2015; Schaye et al. 2015). The simulations are run with a highly modified version of the N-body, smoothed particle hydrodynamics code gadget3 (last described by Springel 2005). Bound galaxies (subhaloes) were identified within the simulations using the subfind algorithm (Springel et al. 2001; Dolag et al. 2009), in the same manner as in the EAGLE simulations (for details see Schaye et al. 2015). EAGLE includes subgrid routines describing radiative cooling (Wiersma, Schaye & Smith 2009a), star formation (Schaye & Dalla Vecchia 2008), stellar evolution, and mass-loss (Wiersma et al. 2009b), the seeding and growth of black holes (BHs) via gas accretion and BH–BH mergers (Rosas-Guevara et al. 2015), and feedback associated with star formation and BH growth (Booth & Schaye 2009). As current cosmological simulations lack the resolution and physics necessary to compute the feedback efficiencies from first principles, the stellar and active galactic nuclei feedback parameters are calibrated such that the simulations of cosmologically representative volumes reproduce the galaxy stellar mass function, galaxy sizes, and BH masses at z ≈ 0. The EAGLE simulations successfully reproduce a range of galaxy properties, including the stellar masses (Furlong et al. 2015) and sizes (Furlong et al. 2017) of galaxies, their luminosities and colours (Trayford et al. 2015), their cold gas properties (Lagos et al. 2015, 2016; Bahé et al. 2016; Marasco et al. 2016; Crain et al. 2017), and the properties of circumgalactic and intergalactic absorption systems (Rahmati et al. 2015, 2016; Oppenheimer et al. 2016; Turner et al. 2016, 2017). The simulations also largely reproduce the cosmic SFR density and relation between specific SFR and galaxy mass (Furlong et al. 2015). The simulations are therefore ideal for comparisons with observed galaxy populations.

2017ApJ...847...42D__Purcell_et_al._2011_Instance_1

The detailed kinematic reconstruction of the Sgr tidal debris by Law & Majewski (2010) used an initial total mass of M⊙ for the Sgr satellite. However, several studies point to a Sgr remnant mass significantly exceeding that value. Ibata et al. (1997) and Ibata & Lewis (1998) estimate lower bounds of 109 M⊙ for the mass of the dwarf today. With the discovery of previously unseen branches of the stream, the total luminosity budget of the progenitor galaxy is now believed to be on the order of 108 L⊙ (Niederste-Ostholt et al. 2010). As a result, recent studies have shifted to using dark-matter halo masses as large as 1011 M⊙ (Purcell et al. 2011; Gòmez et al. 2015) based on halo abundance matching arguments. Such high values are comparable to the mass of the LMC progenitor (see e.g., Jethwa et al. 2016; Peñarrubia et al. 2016) and imply a mass ratio relative to the MW on the order of 1:10. However, unlike the Magellanic Clouds, which may be on their first passage near the MW (Besla et al. 2007), Sgr is known to have experienced multiple close passages in the past. If true, such high Sgr progenitor masses would have important implications for the formation and evolution of the MW disk (e.g., Purcell et al. 2011; Gòmez et al. 2013; D’Onghia et al. 2016). Because the dynamical friction force is proportional to the square of the satellite mass, we expect drag to play a much more important role in slowing down the Sgr satellite and bringing it to closer galactocentric distances. Here, we perform an exploration across orbital angular momentum parameter space analogous to Section 3.1, this time using a Sgr progenitor mass of 6×1010 M⊙ according to the recent estimates of Gibbons et al. (2017). We consider two possibilities: a “slow sinking” scenario in which, as in Section 3, Sgr crosses the MW virial radius at (approximately 8 Gyr ago), and a “rapid sinking” scenario, in which we examine a first infall around , about 4 Gyr ago.

2018MNRAS.478.3890B__Heckman_et_al._2017_Instance_4

Rather than AGN feedback, it is possible that the effects we are seeing are from a different process coeval or prior to the onset of AGN accretion. Several works have pointed out that AGN activity coincides with a recent starburst, with the AGN having significant accretion events at least ∼200 Myr after the starburst has occurred (Davies et al. 2007; Wild et al. 2007; Wild, Heckman & Charlot 2010; Yesuf et al. 2014) giving the neutral material time to propagate out to the impact parameters probed by COS-AGN (Heckman et al. 2017). With a sample of QSO sightlines probing the CGM around 17 low-redshift starburst and post-starburst galaxies, Heckman et al. (2017) have observed a similar signature of enhanced EWs of Ly α, Si iii, and C iv (the latter of which is not measured in our control sample) relative to a control-matched sample (matched in stellar mass and impact parameter). In the range of impact parameters and stellar masses probed by COS-AGN, the strength of our enhanced EW signature is consistent with the values probed by Heckman et al. (2017). However, the results of Heckman et al. (2017) show strong offsets in the kinematics of the gas from the host galaxy (≈100 km s−1; see fig. 5 from Heckman et al. 2017), whereas the COS-AGN sightlines do not (bottom panel of Fig. 6). Assuming that the AGN activity was triggered by the starburst, a minimum delay time of 200 Myr could allow for any starburst-driven winds to dissipate and kinematic offsets to no longer be present at the impact parameters of the COS-AGN sample. Although this starburst picture provides a possible explanation of our observations, we caution that starbursts are not the only astrophysical event linked to AGN accretion activity. For example, mergers that trigger the AGN (Ellison et al. 2011, 2013; Satyapal et al. 2014; Silverman et al. 2014; Goulding et al. 2018) could potentially affect the surrounding CGM gas. Past and future work focusing on the CGM of galaxy mergers can further test this result (Johnson et al. 2014; Hani et al. 2017; Bordoloi et al. in preparation).

2016ApJ...825...47P__Krimm_et_al._2011a_Instance_1

A tidal disruption event (TDE) is an astronomical phenomenon that occurs when a star gets too close to a supermassive black hole in the galaxy center and is disrupted by the tidal force of the black hole. Part of stellar material is bound and accreted by the central black hole, resulting in bright optical, UV, and soft X-ray emission (Rees 1988; Lodato et al. 2015 and references therein). There are a growing number of candidate TDEs being discovered in soft X-ray, ultraviolet, and optical surveys; see Komossa (2015) for a recent review. Recently, three unusual TDE candidates have been discovered by Swift, i.e., Swift J164449.3+573451, Swift J2058.4+0516, and Swift J1112.2-8238 (hereafter Sw J1644+57, Sw J2058+05, and Sw J1112-82 for short, respectively), which have very bright nonthermal hard X-ray and radio emissions (Bloom et al. 2011; Burrows et al. 2011; Krimm et al. 2011a, 2011b; Zauderer et al. 2011; Cenko et al. 2012; Brown et al. 2015). The luminous nonthermal X-ray and radio emissions are thought to be produced by relativistic jets (Bloom et al. 2011; Burrows et al. 2011; Levan et al. 2011; Zauderer et al. 2011; Cao & Wang 2012; Metzger et al. 2012; Wang et al. 2014; Liu et al. 2015). Sw J1644+57 shows a highly variable light curve in X-rays, as observed by the X-ray Telescope on board Swift. At redshift , the isotropic luminosity of the X-ray emission is as high as erg s−1. Sw J2058+05 exhibits a luminous, long-lived X-ray outburst with an isotropic peak luminosity of (at redshift ). Its total isotropic energy (0.3–10 keV) on a timescale of the first 2 months amounts to 1054 erg. Sw J1112-82 was initially also discovered by Swift/BAT (Burst Alert Telescope) in 2011 June as an unknown, long-lived (order of days) γ-ray transient source. It exhibits a similar bright X-ray flare, and its position is consistent with the nucleus of a faint galaxy at (Brown et al. 2015). The peak X/γ-ray luminosity of Sw J1112-82 exceeds .

2019ApJ...879...52S___2010_Instance_1

Kennicutt & Evans (2012) present a compilation of disk-averaged SFR and gas mass surface densities whose values have been calculated in a uniform manner across different galaxy types (including normal disk galaxies and dusty starburst galaxies selected in the IR) and find a power-law index of n ∼ 1.4. However, this result may be an artifact of combining galaxies of different interaction states. For a sample of z ∼ 1–3 MS galaxies, Tacconi et al. (2013) find an index consistent with unity and only a slight offset between their high-redshift sample and a low-redshift sample with similar masses. However, SMGs and other ultra-/luminous infrared galaxies (U/LIRGs) are further offset above the correlation for star-forming disk galaxies even when similar CO-to-H2 conversion factors are used for all galaxy populations (see also Bigiel et al. 2008; Daddi et al. 2010b; Genzel et al. 2010, 2015; Tacconi et al. 2018). In analyses of the resolved star formation properties of nearby disks, a near-unity index for the Schmidt–Kennicutt relation is also found in regimes where the molecular gas dominates the total gas mass surface density (Σgas >9 M⊙ pc−2; e.g., Bigiel et al. 2008, 2010; Schruba et al. 2011). The surface density version of the Schmidt–Kennicutt relation has been evaluated within only eight high-redshift galaxies: SMM J14011+0252 at z = 2.56 (Sharon et al. 2013), EGS 13011166 at z = 1.53 (Genzel et al. 2013), HLS 0918 at z = 5.24 (Rawle et al. 2014), GN20 at z = 4.05 (Hodge et al. 2015), PLCK G244.8+54.9 at z = 3.00 (Cañameras et al. 2017), AzTEC-1 at z = 4.34 (Tadaki et al. 2018), and the two components of HATLAS J084933 at z = 2.41 (Gómez et al. 2018).9 9 Freundlich et al. (2013) and Sharda et al. (2018) also examine the Schmidt–Kennicutt relation at z > 1, but they analyze individually resolved clumps within high-redshift galaxies rather than performing full pixel-by-pixel comparisons. These studies find a range of Schmidt–Kennicutt relation indices (n = 1–2). It is particularly worth noting that Genzel et al. (2013) find that their measured index depends strongly on which spatially resolved extinction correction they apply to their Hα measurements.

2022AandA...666A..83N__Chan_et_al._(2018)_Instance_1

The detection criterion of having the full interval α ± σα inside the prior range [0.8, 1.2] (e.g., Ata et al. 2018; Chan et al. 2018) has commonly been employed in the literature. However, we can observe from these analyses that the distribution of the α parameter in general seems to be more Gaussian than what we find in our analyses (left panels in Figs. 3 and 4). A possible reason for this is the lower redshift we considered here, where the contribution from nonlinear effects is stronger. However, results from Villaescusa-Navarro et al. (2017), who investigated the BAO detection from 21 cm signal for the SKA case for the redshift range 0.35  z  3.05, also showed α distributions with clear deviations from a perfect Gaussian (but note the smaller number of simulations employed there, namely, 100). As pointed out by Chan et al. (2018) and Abbott et al. (2022), a natural consequence from having a (approximate) Gaussian distribution is a reasonable concordance among the three different error measurements, that is, ⟨σα⟩∼σ68 ∼ σstd. This is not our case, as can be seen from Tables 2 and 3 (as well as from Tables 4 and 5), indicating that ⟨σα⟩ is not meaningful or representative of the error in the α measurements for individual mock realizations (see also Figs. 3 and 4). Our results show that compared to ⟨σα⟩, the errors given by σ68 are overestimated by ∼18% to 33%, from the smallest to the highest z-bins when the ACF is used, but is underestimated by ∼50% for APS. Moreover, comparing the expected σ α m $ \sigma^m_\alpha $ obtained fitting the mean Cℓ and ω(θ) to the average ⟨σα⟩, we find a reasonable agreement for intermediate and higher z-bins, but not for the lower z-bins, in particular, for the ACF estimator. In addition, although σstd agrees better with σ68, we still find non-negligible differences among them, which confirms our α distributions as non-Gaussian (a larger σstd indicates non-Gaussian tails). These reasons motivated our choice of using the α values instead of α ± σα ranges, which belong to the interval [0.8, 1.2] as a criterion for a BAO detection (defining the Nd fraction), as well as our choice of using the 68% spread of the α distributions, σ68, as the representative error in our measurements.

2019MNRAS.486.1608N__Cicone_et_al._2014_Instance_1

The clearest examples of host–AGN interaction are arguably found in nearby brightest cluster galaxies. The AGN in these systems have been shown to deposit vast amounts of energy into the surrounding intracluster medium via heating and (mega-parsec scale) jets both observationally and by means of modelling (e.g. Binney 2004; Scannapieco, Silk & Bouwens 2005; Gitti, Brighenti & McNamara 2012, for a review; English, Hardcastle & Krause 2016), which maintain the hot-gas reservoirs in these systems, prevent cooling flows, and thus suppress star formation (e.g. Binney & Tabor 1995; Li et al. 2015). In lower density environments (where the majority of galaxies live), the empirical picture is much less clear. Direct observational evidence for AGN feedback on galactic scales in such environments remains sparse. More specifically, while outflows have been observationally detected in a number of instances around AGN in various gas phases, most of these detections have been made in ultra-luminous infrared galaxies or some of the closest quasar–host galaxies (e.g. Rupke, Veilleux & Sanders 2005b; Nyland et al. 2013; Harrison et al. 2014), with only a few examples where the AGN have been shown to couple with kpc-scale outflows that are capable of impacting star-formation on galactic scales (for a review Cicone et al. 2014; Harrison 2017). Thus, it is still unclear to what extent the general AGN population could drive kpc-scale galactic outflows capable of possibly limiting or quenching star-formation in the local Universe. Indeed, recent observational work has cast doubt on the ability of AGN to directly regulate star formation in the nearby Universe. For example, Schawinski et al. (2014) found that black hole accretion occurs preferentially in quenched galaxies that experienced a rapid decay of their star-formation rates. Based on stellar population analysis, such a time delay between the peak of star formation and the onset of AGN activity has been reported to be of at least several dynamical time-scales (e.g. Kaviraj et al. 2015; Shabala et al. 2017, for radio and e.g. Schawinski et al. 2007; Kaviraj 2009; Wild, Heckman & Charlot 2010 for optical). AGN may therefore not play a significant direct role in regulating their associated star formation episodes as they would not couple directly to the cold-gas reservoir (see, e.g. the models of Kaviraj et al. 2011). Using observations for cold-gas outflows, this has been directly confirmed for radio AGN, which are found to couple mainly to residual gas in galaxies where the gas reservoir is already significantly depleted (e.g. Sarzi et al. 2016). A fuller understanding of the role of AGN in regulating star formation demands a direct study of whether outflows of neutral material (which are ultimately responsible for quenching star formation) are more likely launched in AGN hosts. Most importantly, a quantitative statement about the putative role of AGN in influencing the evolution of their host galaxy requires a study that employs a complete sample of such AGN in the local Universe. Performing such an analysis is the purpose of this paper.

2017ApJ...834..178Y__Tachihara_et_al._2007_Instance_1

In order to investigate the gas kinematics at an early evolutionary stage and the formation of Keplerian disks, we conduct ALMA observations toward three candidate young protostars, Lupus 3 MMS, IRAS 15398−3559, and IRAS 16253−2429. They are selected from our SMA sample (Yen et al. 2015a). These three protostars all have relatively low protostellar masses (0.1 M⊙), inferred from the infalling motions in their protostellar envelopes, and they do not show clear signs of a spin-up rotation on a 1000 au scale; i.e., no signatures of Keplerian disks are seen in our SMA observations (Yen et al. 2015a). Lupus 3 MMS is a Class 0 protostar with a bolometric luminosity (Lbol) of 0.41 L⊙ and a bolometric temperature (Tbol) of 39 K in the Lupus 3 cloud at a distance of 200 pc (Tachihara et al. 2007; Comerón 2008; Dunham et al. 2013). Our SMA results suggest that the protostellar mass in Lupus 3 MMS can be as low as 0.1 M⊙ (Yen et al. 2015a). IRAS 15398−3559 is a Class 0/I protostar with an Lbol of 1.2 L⊙ and a Tbol of 61 K in the Lupus 1 cloud at a distance of 150 pc (Froebrich 2005; Comerón 2008). Early single-dish observations of its CO outflow suggest that IRAS 15398−3559 is close to face on (van Kempen et al. 2009). Recent SMA and ALMA observations show that it is actually closer to edge on (Oya et al. 2014; Bjerkeli et al. 2016). With this new estimated inclination angle (∼70°), our SMA data suggest a low protostellar mass (0.1 M⊙) and a low specific angular momentum in the protostellar envelope (∼1 × 10−4 km s−1 pc; Yen et al. 2015a). IRAS 16253−2429 is a Class 0 protostar with an Lbol of 0.24 L⊙ and a Tbol of 36 K in the ρ Ophiuchus star-forming region at a distance of 125 pc (Dunham et al. 2013). Both CARMA and our SMA results suggest that its protostellar mass is 0.1 M⊙ (Tobin et al. 2012a; Yen et al. 2015a). These three protostars are all embedded in dense cores with masses ≳0.5 M⊙ (Froebrich 2005; Tachihara et al. 2007; Enoch et al. 2009). Therefore, they are excellent targets by which to study the gas motions on a 100 au scale at an early evolutionary stage.

2022MNRAS.517.5032D__Dihingia_et_al._2021_Instance_1

In our study, we consider that the accretion disc is threaded by the poloidal magnetic field lines. The initial poloidal field lines are prescribed by implementing a vector potential Aϕ following Zanni et al. (2007) and Vourellis et al. (2019). The functional form of the vector potential is given by (6)$$\begin{eqnarray} A_\phi \propto \left(r \sin \theta \right)^{3/4} \frac{m^{5/4}}{\left(m^2 + \tan ^{-2}(\theta -\pi /2)\right)^{5/8}}. \end{eqnarray}$$The parameter m(= 0.1) is related to the initial inclination of the field lines and it also determines the magnetic flux of the system. The parameter m play crucial role in the launching of Blandford–Payne type wind from the accretion disc (Blandford & Payne 1982; Dihingia et al. 2021). The initial strength of the poloidal magnetic field is determined by the choice of the plasma-β parameter at the truncation radius rtr on the equatorial plane as, $\beta _{\rm tr} = p_{\rm gas}^{\rm tr}/p_{\rm mag}^{\rm tr}$. Here, superscript ‘tr’ denotes quantities calculated at the r = rtr. Following our motivation, we carry out eight axisymmetric simulation models, by choosing different truncation radius (rtr), initial plasma-β (βtr), effective resolution (with AMR), and magnetic resistivity. For axisymmetric models, we consider the highest effective resolution to be 2048 × 1024 (with three refinement levels). The list of input parameters, effective resolution, and the final simulation time (tfinal) for all the simulation models are shown in the Table 1. Out of all these models, we consider 2D40AH to be our reference model for the sake of explanation and comparison. In Fig. 1, we show the initial density distribution (log (ρ/ρtr)) and the initial gas pressure distribution $(\log (p_{\rm gas}/p_{\rm gas}^{\rm tr}))$ for the reference model at panels (a) and (b), respectively. In panel Fig. 1(a), we also show the initial magnetic field lines in terms of grey lines. In Fig. 1(b), the white line represent the boundary of plasma-β = 1. In the figure, the density distribution follows equation (2) outside the truncation radius (rtr = 40). Near the equatorial plane, the matter distribution is gas pressure dominated, and far from the equatorial plane and inside the truncation radius, the matter distribution is magnetic pressure dominated.

2019MNRAS.482.3950S__Shultz_et_al._2015_Instance_1

As a first step to analysis of NU Ori’s magnetic field, least-squares deconvolution (LSD) profiles were extracted using a line mask developed from an extract stellar request from the Vienna Atomic Line Database 3 (VALD3; Piskunov et al. 1995; Ryabchikova et al. 1997, 2015; Kupka et al. 1999, 2000) using the stellar parameters determined for NU Ori Aa (Teff = 30.5 ± 0.5 kK, log g = 4.2 ± 0.1) by Simón-Díaz et al. (2011). These parameters were selected since Petit et al. (2008) identified the Aa component as the magnetic star, given that the Stokes V signature is much wider than the $v$sin i of the secondary component. The line mask was cleaned of all H lines, as well as lines strongly blended with H line wings, lines in spectral regions strongly affected by telluric contamination, lines blended with nebular or interstellar features, and lines in spectral regions affected by ripples. While He lines are often removed due to substantial differences between magnetometry results obtained from He versus metallic lines (e.g. Shultz et al. 2015, 2018b; Yakunin et al. 2015), in this case He lines were left in the mask since the majority of the Stokes V line flux comes from these lines, and the Stokes V profiles extracted using a line mask with He lines excluded did not result in detectable Zeeman signatures. Because of the very high $v$sin i of the Aa component, LSD profiles were extracted using a velocity range of ±600 km s−1 (in order to include enough continuum for normalization) and a velocity pixel size of 7.2 km s−1, or four times the average ESPaDOnS velocity pixel (thus raising the per pixel S/N by about a factor of 2). The significance of the signal in Stokes V was evaluated using false alarm probabilities (FAPs), with observations classified as definite detections (DDs), marginal detections (MDs), or non-detections (NDs) according to the criteria described by Donati, Semel & Rees (1992) and Donati et al. (1997). Since FAPs essentially evaluate the statistical significance of the Stokes V signal inside the stellar line by comparing it to the noise level, they are primarily sensitive to the amplitude of Stokes V, which unlike 〈B$z$〉 is not strongly dependent on rotational phase. FAPs are thus a complementary means of checking for the presence of a polarization signal, the principal advantage being that they can detect a magnetic field even at magnetic nulls, i.e. 〈B$z$〉 = 0.

2020ApJ...891...28T__Lind_et_al._2015_Instance_1

Most GCs are now found to host multiple populations through photometry and spectroscopy (e.g., Carretta et al. 2010b; Mészáros et al. 2015; Milone et al. 2015; Piotto et al. 2015; Tang et al. 2017, 2018). Chemical abundances from spectroscopic data suggest that GCs have a group of so-called “second generation” (SG) stars with enhanced N and Na (sometimes He and Al), but depleted C and O (sometimes Mg). These kinds of stars presumably are only formed in the dense environments of GCs. Therefore, identifying field stars with an SG-like chemical pattern is a feasible way to find a link between field stars and GC ejection/dissolution. Thanks to large spectroscopic surveys, the search for these chemically peculiar stars is becoming more efficient. Using high spectral resolution surveys, multiple elements, like C, N, O, Na, Mg, and Al, can be measured, depending on the wavelength range and signal-to-noise ratio. Toward this, the Apache Point Observatory Galactic Evolution Experiment (APOGEE; Majewski et al. 2017) and Gaia-ESO survey have led to the discovery of a large group of N-rich field stars (Lind et al. 2015; Fernández-Trincado et al. 2016, 2017, 2019a; Martell et al. 2016; Schiavon et al. 2017). While high-resolution spectra give more elements for a detailed investigation of their chemical history, low-resolution spectra can supposedly extend the search for N-rich field stars toward fainter and more numerous samples (Martell & Grebel 2010; Martell et al. 2011; Koch et al. 2019). Simultaneously observing 4000 stars with fibers makes LAMOST an unprecedented machine in collecting low-resolution stellar spectra. Using the CN–CH band features around 4000 Å, we have identified ∼40 N-rich field stars8 8 Also called CN-strong, CH-normal stars in Paper I. in LAMOST DR3 (Tang et al. 2019, hereafter Paper I). The derived N abundances of these stars are clearly higher than those of the metal-poor field stars, indicating that (1) our sample is a bona fide sample of N-rich field stars and (2) the classical extra-mixing theory may not work for these stars. Moreover, a substantial fraction of retrograding N-rich field stars suggest that some N-rich field stars may be accreted. In this work, we expand our sample to ∼100 N-rich field stars in LAMOST DR5 (Section 2), making it more robust for drawing statistical conclusions, especially for the GC origin of these field stars. We put forward a detailed analysis of high-resolution chemical abundances and kinematics (Sections 3 and 4) to discuss the origins of these N-rich field stars (Section 5). As the second paper of this series, we will also call this present work Paper II.

2017AandA...601A..64Z__Milosavljević_&_Nakar_2006_Instance_1

The distribution function of the electrons inside the precursor can be formally determined through a Lorentz transform of the corresponding distribution function in the shock frame, and the latter can be obtained by solving a stationary transport equation with a loss term corresponding to the finite residence time spent in the precursor. Here, we approximate this distribution function in the precursor frame as follows: (3)\begin{equation} \frac{{\rm d}n_{\rm e\vert\rm p}}{{\rm d}\gamma_{\rm e}}\,\simeq\,\frac{R^2}{r^2}\, \frac{\vert s-1\vert}{\gamma_{\rm m\vert p}}\left(\frac{\gamma_{\rm e}}{\gamma_{\rm m\vert p}}\right)^{-s}\, 2\Gamma_{\rm sh}^2n_{\rm u}\,\Theta\left[r_{\rm p}(\gamma_{\rm e})-r\right]. \label{eq:dndg1} \end{equation}dne|pdγe ≃ R2r2 |s−1|γm|pγeγm|p−s 2Γsh2nu Θ[rp(γe)−r].The Heaviside function models the finite length scale up to which particles of a given Lorentz factor can travel. This precursor length scale is related to the (upstream frame) residence time tres through \hbox{$r_{\rm p}\,=\,R+c t_{\rm res}\left(1-\beta_{\rm sh}\right)\,\simeq\,R+ct_{\rm res}/(2\Gamma_{\rm sh}^2)$}rp = R+ctres1−βsh ≃ R+ctres/(2Γsh2). The residence time can be calculated as the time it takes to deflect the accelerated electron by an angle ~1/Γsh (Achterberg et al. 2001; Milosavljević & Nakar 2006; Pelletier et al. 2009). If the background magnetic field controls the transport of the accelerated electrons, this residence time is \hbox{$t_{\rm res}^{(1)}\,\simeq\,\Gamma_{\rm sh}^{-1}\gamma_{\rm e} m_{\rm e} c/(e B_{\rm u})$}tres(1) ≃ Γsh-1γemec/(eBu). In contrast, if pitch angle scattering in a micro-turbulence of length scale λδB governs the return of particles to the shock, \hbox{$t_{\rm res}^{(2)}\,\simeq\,\Gamma_{\rm sh}^{-2}\gamma_{\rm e}^2m_{\rm e}^2c^3/(\lambda_{\delta B}e^2\delta B_{\rm p}^2)$}tres(2) ≃ Γsh-2γe2me2c3/(λδBe2δBp2). Whether one or the other occurs depends on γe and the hierarchy between Bu and δBp. As the scattering frequency in a micro-turbulence scales as \hbox{$\gamma_{\rm e}^{-2}$}γe-2, while the gyrofrequency scales with \hbox{$\gamma_{\rm e}^{-1}$}γe-1, we expect the background field to control the residence time at higher energies. In order to bracket this realistic scenario, we consider in the following the above two extremes: (1) where regular deflection in a background field dominates at all energies and (2) where stochastic deflection in a small scale micro-turbulence of uniform energy density dominates at all energies. Correspondingly, we write \hbox{$r_{\rm p}(\gamma_{\rm e})=R+\Delta_i\gamma_{\rm e}^i$}rp(γe)=R+Δiγei, with i = 1,2 for models (1) or (2); \hbox{$\Delta_i=ct_{\rm res}^{(i)}/(2\Gamma_{\rm sh}^2)$}Δi=ctres(i)/(2Γsh2).

2021AandA...650A.155Z__Oh_et_al._2012_Instance_2

Many factors can affect the prevalence of AGN activity. One important question is how gas is brought down to the galaxy center to fuel supermassive black holes (SMBHs). In the literature, two kinds of mechanisms are proposed. One is the internal secular evolution process. The torque induced by non-axisymmetric galactic structures can drive slow and significant inflow (Kormendy & Kennicutt 2004; Hopkins & Quataert 2011; Sellwood 2014; Fanali et al. 2015). The galactic bar is one of the most prominent non-axisymmetric structures and it exists in about 40% of spiral galaxies (Oh et al. 2012). In addition, there is evidence demonstrating that bars can enhance star formation in the central regions of galaxies (e.g. Oh et al. 2012; Chown et al. 2019). However, the question of whether galactic bars can significantly affect AGN activity is still under debate (Arsenault 1989; Mulchaey & Regan 1997; Oh et al. 2012; Galloway et al. 2015; Goulding et al. 2017; Alonso et al. 2018). Other mechanisms, such as galaxy merger and interaction, are also expected to displace the angular momentum of the gas and transport the gas inward (e.g. Hopkins et al. 2006; Di Matteo et al. 2008; Bhowmick et al. 2020). Similarly to studies of secular evolution, observational evidence for this scenario is also mixed. Some studies have found significant environmental dependence of AGN activity (e.g. Koulouridis et al. 2006; Koss et al. 2010; Ellison et al. 2011; Sabater et al. 2013; Khabiboulline et al. 2014; Lackner et al. 2014; Satyapal et al. 2014; Hong et al. 2015; Kocevski et al. 2015; Goulding et al. 2018; Gao et al. 2020), while others have found no or only weak environmental effects (e.g. Grogin et al. 2005; Li et al. 2006a, 2008; Pierce et al. 2007; Ellison et al. 2008; Gabor et al. 2009; Darg et al. 2010; Wang & Li 2019; Man et al. 2019). The contradictory results may be caused by the difference in AGN selection criterion, observational bias, control sample, and environmental indicator used. As we show below, understanding the environmental effects on AGNs also requires knowledge about the evolutionary status of their host galaxies, as it can help us to better understand how to construct control samples and to adopt appropriate environmental indicators.

2015AandA...584A..32M__Sargent_et_al._2010_Instance_1

Nearly all of the cm-wavelength radio emission from star-forming galaxies, such as SMGs, is non-thermal synchrotron radiation from relativistic electrons accelerated in supernova (SN) remnants produced by the short-lived, high-mass OB-type stars (M ≳ 8 M⊙; main-sequence lifetime τMS ≲ 30 Myr). Because SNe trace the recent/on-going star formation, the radio synchrotron emission has the potential to trace the spatial scales on which star formation is occurring. This connection between radio emission and star formation is strongly supported by the close infrared (IR)-radio correlation observed in galaxies (e.g. Helou et al. 1985; Beck & Golla 1988; Xu et al. 1992; Condon 1992; Yun et al. 2001; Bell 2003; Tabatabaei et al. 2007; Murphy et al. 2008; Sargent et al. 2010; Morić et al. 2010; Dumas et al. 2011). On the basis of this correlation, the IR-emitting region of a star-forming galaxy is expected to be comparable in size to that of radio continuum emission. However, the most recent studies of the sizes of IR-emitting regions of SMGs based on continuum imaging observations with the Atacama Large Millimetre/submillimetre Array (ALMA) show that these are significantly smaller than SMG radio sizes presented in the literature (Simpson et al. 2015a; Ikarashi et al. 2015). A possible explanation for this discrepancy, as suggested by Simpson et al. (2015a), is cosmic ray (CR) diffusion in the galactic magnetic field away from their acceleration site, which would render larger radio sizes. To test this further here we present a study of radio sizes of SMGs from a well selected sample of SMGs in the Cosmic Evolution Survey (COSMOS; Scoville et al. 2007) deep field using radio data from the Karl G. Jansky Very Large Array (VLA)-COSMOS 3 GHz Large Project (1σ noise of 2.3 μJy beam-1, angular resolution \hbox{$0\farcs75$}0 .̋ 75; Smolčić et al., in prep.). We describe the SMG sample and the employed VLA data in detail in Sect. 2. The 3 GHz images are presented in Sect. 3, and the analysis (size measurements and radio spectral indices) are presented in Sect. 4. We compare our results with literature studies in Sect. 5, discuss the results in Sect. 6, and summarise the main results of the paper in Sect. 7.

2022ApJ...926...85S__Ehrenreich_et_al._2020_Instance_2

As in Flowers et al. (2019), to compare our model transmission spectra directly against the Ehrenreich et al. (2020) results, we must calculate the transmission spectra as a function of orbital phase throughout the duration of transit. To account for orbital phase dependencies, we apply the following procedure:1.Account for phase-dependent backlighting of the planet (i.e., stellar limb-darkening effects). At different points of its transit, the planet will occult regions of its host star of varying brightness. Furthermore, at a fixed orbital phase, different regions of the planet’s limb will be backlit by varying intensities of stellar light. Similar to Flowers et al. (2019), we calculate the normalized stellar intensity at the center of each cell of the 2D projected planetary grid produced by our GCM at each modeled orbital phase of the planet. We use the quadratic limb-darkening coefficients reported by Ehrenreich et al. (2020) to establish the stellar center-to-limb intensity profile, and we take into account the 89.°623 orbital inclination of WASP-76b (Ehrenreich et al. 2020) to determine where the planet resides on the stellar disk as a function of its orbital phase. We make the assumption of constant impact parameter b over the course of transit. 9 9 In reality, a planet on an inclined orbit will not have a constant b over the entire duration of transit; rather, the planet’s distance from the stellar equator will be decreased at ingress and egress, reaching its maximum at center of transit. Our tests reveal that, for WASP-76b, the relative error induced by the constant b assumption is on the order of 4% in distance, which results in a change on the order of 1 m s−1 at the blueshift level (see Section 3.1). Hence, our b treatment is justified. This procedure allows us to calculate a backlighting factor f, which ranges from 0 to 1, effectively replacing the constant I λ,0 from Equation (3) with a variable Iλ,0×f(θ′,z,φ) , for a given orbital phase φ and 2D projected polar angle θ′ .2.Account for the decreasing of the continuum by interpolating a light curve produced by the batman code (Kreidberg 2015). Step 1 ensures that less light is transmitted through the planet’s atmosphere than a uniform stellar disk would emit. Step 2 further enforces that the inner, optically thick core of the planet is simulated crossing a limb-darkened star, as opposed to a star of uniform brightness.3.Account for the planet’s rotation over the course of transit. Because the planet is continually rotating as it travels across the face of its host star, we must transform the GCM coordinate system so that the correct observer-facing hemisphere is modeled at each instance during transit. For simplicity, we assume zero obliquity, which allows us to calculate the coordinate transform simply by assigning a linear offset to each planetary longitude; i.e., ϕ rotated = ϕ + φ.

2022MNRAS.513.4464T__Hopkins_et_al._2020_Instance_2

Galactic winds: Galactic winds driven by CRs have often been simulated in two limits: a diffusion-dominated regime, due possibly to ‘extrinsic confinement’, where CRs are scattered by extrinsic turbulence, and/or due to various wave damping mechanisms (e.g. ion neutral damping) and streaming-dominated ‘self confinement’, where CRs are confined by Alfven waves they produce via the gyroresonant streaming instability. In the diffusive ‘extrinsic confinement’ case, CRs do not heat the gas.19 In the streaming dominated ‘self confinement’ case, CR transport heats gas at a rate vA · ∇Pc. The diffusive case fits γ ray observations better, because CRs can propagate out of the galaxy faster (Chan et al. 2019). It is also much better at driving winds, because the CRs do not suffer strong energy losses via Alfven wave heating (Wiener et al. 2017b; Hopkins et al. 2020). However, we expect self-confinement to be very strong at the ∼GeV energies where CR energy peaks (Kulsrud & Pearce 1969; Farmer & Goldreich 2004; Wiener et al. 2013), while extrinsic compressible turbulence is strongly damped at small scales, and unlikely to efficiently scatter ∼GeV CRs (Yan & Lazarian 2002). Thus, CR winds should be streaming dominated and relatively inefficient. The CR staircase changes these dichotomies by changing the structure of the wind. We have seen how CR pressure can build up in streaming dominated simulations, due to trapping at bottlenecks. This increases mass outflow rates, similar to the effect of increased opacity in radiative outflows. In CR streaming simulations of isothermal winds where the CR acoustic instability arose, Quataert et al. (2022a) found an increase in wind mass loss rates by an order of magnitude, compared to analytic models without a CR staircase, illustrating the potential impact of CR staircases. High-resolution cosmological zoom simulations of CR staircases are actually well within reach. As seen in Appendix Section B, all that is required is that the diffusion length $l_{\rm diff} \sim \kappa /c_{\rm s} \sim 2 \, {\rm kpc} \, \left(\frac{\kappa }{10^{29} {\rm cm^2 s^{-1}}} \right)\left(\frac{c_{\rm s}}{150 \, {\rm km \, s^{-1}}} \right)^{-1}$ is resolved. However, to date only the FIRE collaboration has implemented the two moment method (capable of dealing with CR streaming) in such simulations, and – in contrast to, for instance, van de Voort et al. (2021) – the plasma β in their winds is too high for the acoustic instability to develop (Hopkins et al. 2020). But alternate setups where CR staircases appear are certainly numerically feasible.

2016ApJ...826..168X__Bai_2014_Instance_1

MRI is considered to be the most promising mechanism driving angular-momentum transport in protoplanetary disks (Balbus & Hawley 1991; Brandenburg et al. 1995; Hawley et al. 1995; Balbus et al. 1996; Balbus & Hawley 1998). However, protoplanetary disks are cold, dense, and, therefore, poorly ionized. The low level of ionization tends to decouple the disk gas from magnetic fields, which generates non-ideal MHD effects: Ohmic dissipation, ambipolar diffusion (AD), and the Hall effect (e.g., Armitage 2011; Turner et al. 2014). These effects quench MRI in different ways: Ohmic dissipation originates from collisions between electrons and neutrals, AD from collisions between ions and neutrals, and the Hall effect from drift between electrons and ions (Fleming et al. 2000; Sano & Stone 2002; Bai & Stone 2011). Ohmic dissipation operates in high-density regions with weak field, AD dominates in highly ionized and low-density regions, and the Hall effect lies in between (Fleming & Stone 2003; Bai & Stone 2011; Bai 2014). So far, the effect of Ohmic dissipation has been best studied. Investigations show the layered accretion in the inner disk, where the midplane region is “dead” due to low ionization while the surface layer is “active” due to sufficient ionization (Gammie 1996; Jin 1996; Fleming et al. 2000; Fleming & Stone 2003; Turner et al. 2007; Ilgner & Nelson 2008; Oishi & Mac Low 2009; Okuzumi & Hirose 2011). Recent works that take into account both Ohmic dissipation and AD show that AD may render the surface layer and portions of the outer disk inactive (Bai & Stone 2011; Landry et al. 2013; Kalyaan et al. 2015). Bai & Stone (2013) find that MRI is completely suppressed in the inner disk and a strong magnetocentrifugal wind is launched. Three-dimensional simulations that include all three non-ideal MHD effects are also performed (Bai 2014, 2015; Lesur et al. 2014; Simon et al. 2015). In the inner disk, the influence of the Hall effect on midplane angular-momentum transport depends on the orientation of the vertical magnetic field with the disk rotation axis. When the field is aligned with the axis, the enhanced Maxwell stress promotes angular-momentum transport. When the field is anti-aligned with the axis, the midplane remains quiescent. In the outer disk, the Hall effect has little influence on the disk turbulence. Although the inclusion of AD and the Hall effect substantially changes the level of turbulence in the protoplanetary disks, the feature that the viscosity is low in the inner disk and high in the outer disk is still valid. In this study, we assume that gas giant planets form in situ via the core accretion scenario, which implies that their formation locations are always in the low-viscosity region. Since in this study we focus on the relation between photoevaporation and planet formation and gap opening by planets in the disk, we adopt Ohmic dissipation to represent the non-ideal MHD effects on the MRI. We consider that this simplification has little influence on our main calculation results.

2020AandA...643A.149S__Nilsson_et_al._2011_Instance_1

An increasing number of recent works focus on the study of high-redshift Lyman-α emitters (LAEs), objects showing prominent rest-frame Lyα emission within a spectrum (usually) devoid of other line features (e.g. Cassata et al. 2011; Nakajima et al. 2018). The spectral properties of LAEs are usually interpreted as coming from young (≲50 Myr) and low-mass (M*​   ​1010 M⊙) galaxies (e.g. Wilkins et al. 2011; Amorín et al. 2017; Hao et al. 2018; Santos et al. 2020) with small rest-frame UV half-light radii (R​ ≲ ​1 − 2 Kpc, as in e.g. Møller & Warren 1998; Lai et al. 2008; Bond et al. 2012; Guaita et al. 2015; Kobayashi et al. 2016; Ribeiro et al. 2016; Bouwens et al. 2017a; Paulino-Afonso et al. 2018) which are actively star forming (SFR​ ∼ ​1 − 100 M⊙ yr−1) and dust poor (dust attenuation AV​   ​0.2, see e.g. Gawiser et al. 2006, 2007; Guaita et al. 2011; Nilsson et al. 2011; Bouwens et al. 2017b; Arrabal Haro et al. 2020). When observed at high redshift, isolated and grouped LAEs appear to represent the progenitors of present-day galaxies and clusters, respectively, providing extremely valuable insights into structure formation (e.g. Matsuda et al. 2004, 2005; Venemans et al. 2005; Gawiser et al. 2007; Overzier et al. 2008; Guaita et al. 2010; Mei et al. 2015; Bouwens et al. 2017b; Khostovan et al. 2019). A basic statistical tool to study the population of high-z LAEs is the description of their number density at a given redshift as a function of line luminosity (LLyα), namely the Lyα luminosity function (LF; see e.g. Gronke et al. 2015, for a theoretical approach). Several recent works have focused on the construction of the Lyα LF at z​ ≥ ​2 (Gronwall et al. 2007; Ouchi et al. 2008; Blanc et al. 2011; Clément et al. 2012; Konno et al. 2016; Sobral et al. 2017, 2018a) by making use of deep observations of narrow sky regions (up to few squared degrees, as in e.g. Matthee et al. 2014, 2017b; Cassata et al. 2015; Ono et al. 2018). Their findings describe a Lyα LF that follows a Schechter function (Schechter 1976) at relatively faint line luminosity (i.e. LLyα ≲ 1042.5, see e.g. Ouchi et al. 2008; Konno et al. 2016; Sobral et al. 2016; Matthee et al. 2017a), a regime mostly occupied by low-mass star-forming galaxies (e.g. Hu et al. 1998; Kudritzki et al. 2000; Stiavelli et al. 2001; Santos et al. 2004; van Breukelen et al. 2005; Gawiser et al. 2007; Rauch et al. 2008; Guaita et al. 2011).

2019AandA...631A.109W__Rivera_et_al._(2017)_Instance_1

With the advent of the LOw Frequency ARray (LOFAR; Röttgering et al. 2011; van Haarlem et al. 2013) which combines a large field of view with high sensitivity on both small and large angular scales, we can now study the FIRC at lower frequencies where the contribution from thermal free-free emission is even less important than at 1.4 GHz. Operating between 30 and 230 MHz, LOFAR offers complementary information to the wealth of data collected at higher frequencies. Using deep LOFAR 150 MHz observations in the 7 deg2 Boötes field (Williams et al. 2016), Calistro Rivera et al. (2017) studied the FIRC at 150 MHz from z ∼ 0.05 out to z ∼ 2.5. They found fairly mild redshift evolution in the logarithmic IR to radio luminosity ratio in the form of qIR ∼ (1 + z)−0.22 ± 0.05. However, if the FIRC is non-linear (i.e. the logarithmic slope is different from one), then it implies that the qIR parameter would depend on luminosity. Therefore the reported redshift dependence of qIR may simply be a consequence of the non-linearity of the FIRC (Basu et al. 2015) as the mean SFR of galaxies is generally larger at higher redshifts (e.g., Hopkins & Beacom 2006; Madau & Dickinson 2014; Pearson et al. 2018; Liu et al. 2018; Wang et al. 2019). Based on LOFAR observations of the Herschel Astrophysical Terahertz Large Area Survey (H-ATLAS; Eales et al. 2010) 142 deg2 North Galactic Pole (NGP) field (Hardcastle et al. 2016), Gürkan et al. (2018) found that a broken power-law (with a break around SFR ∼1 M⊙ yr−1) compared to a single power law is a better calibrator for the relationship between RC luminosity and SFR, possibly implying additional mechanisms for generating cosmic rays and/or magnetic fields. Also using LOFAR data in the NGP field, Read et al. (2018) found evidence for redshift evolution of the FIRC at 150 MHz. Heesen et al. (2019) studied the relation between radio emission and SFR surface density using spatially resolved LOFAR data of a few nearby spiral galaxies. They found a sublinear relation between the resolved RC emission and the SFR surface densities based on GALEX UV and Spitzer 24 μm data.

2019AandA...632A.129W__Shodhan_et_al._2000_Instance_1

Coronal mass ejections (CMEs) are intense solar explosive eruptions during which large amounts of plasma and magnetic field from the solar atmosphere are ejected into interplanetary space. The interplanetary manifestations of CMEs (ICMEs; Kilpua et al. 2017) can be measured by a spacecraft at about 1 AU and exhibit the following characteristics: increase in total magnetic magnitude (Cane & Richardson 2003), helium abundance (Hirshberg et al. 1972; Zwickl et al. 1982; Richardson & Cane 2004), average iron ionization (Lepri et al. 2001; Lepri & Zurbuchen 2004), and O7+ abundance (Richardson & Cane 2004; Wang & Feng 2016); decrease in proton temperatures and proton densities (Gosling et al. 2001; Zhang et al. 2013); counterstreaming suprathermal electron (CSE) strahls and declining speed (Zwickl et al. 1982; Gosling et al. 1987; Shodhan et al. 2000; Burlaga et al. 2001). A subset of ICMEs was defined as magnetic cloud (MC) by Burlaga et al. (1981) empirically using the following properties: (1) the magnetic field strength is higher than average, (2) a smooth change in field direction as observed by a spacecraft passing through the cloud, and (3) low proton temperature compared to that of the ambient proton. Magnetic clouds usually have magnetic flux rope structures, and they are the main source of major geomagnetic storms (Burlaga et al. 1981; Webb et al. 2000; Huttunen et al. 2002; Zhang et al. 2007). Observations at 1 AU show that 30 − 40% of ICMEs are MCs, and this percentage depends on the solar cycle (Richardson & Cane 2004). However, CMEs are usually assumed to have magnetic flux rope structures near the Sun because of their helical shapes (Canfield et al. 1999; Liu et al. 2010; Rust & Kumar 1996). This begs the question of whether or not nonMC ICMEs also have flux rope structures. The journal of Solar Physics once made a special issue to address this question (Gopalswamy et al. 2013a). A comparative study of 23 MCs and 31 nonMC ICMEs was completed, and the source regions of the 54 ICMEs were located within ±15° longitude from the disk center. Yashiro et al. (2013) found no significant difference between the structures of the post-eruption arcades of MCs and nonMC ICMEs during launch. Gopalswamy et al. (2013b) observed that MCs and nonMC ICMEs have significant enhancement in Fe and O charge states, and Fe and O charge-state measurements are positively correlated with flare properties, including flare size and soft X-ray flare intensity. Their observations suggest that these CMEs have similar explosive environment and flux rope structures near the Sun. Furthermore, some studies indicate that CMEs associated with MCs tend to propagate along the Sun–Earth line, whereas nonMC events are deflected away from the Sun–Earth line (Kim et al. 2013; Mäkelä et al. 2013; Zhang et al. 2013). Therefore, many researchers believe that all ICMEs have magnetic flux rope structures and that the nonMC events are due to observational limitations, that is, that the observing spacecraft crosses the flanks of the ropes and therefore the ICMEs appear as nonMCs. This has been shown by some multi-satellite-observed ICMEs, namely spacecraft farther from the axis detect less clear flux rope signatures than centrally crossing spacecraft for the same event (Cane et al. 1997; Kilpua et al. 2011).

2019ApJ...878..108L___1991_Instance_1

A charged particle moving in the electromagnetic field can feel Landau–Lifshitz radiation reaction force due to synchrotron radiation, thus modifying the motion of the charged particle significantly when taking into account the radiation reaction force. Recent observations have demonstrated that there is strong evidence that a magnetic field of several hundred Gauss exists in the vicinity of the supermassive black hole at the center of the Milky Way (Eatough et al. 2013). A dynamo mechanism from an accretion disk around a black hole accounts for the appearance of such a magnetic field (Punsly 2001; Brandenburg et al. 1995; Hawley et al. 1996). There is also clear evidence that the magnetic field on the surface of the neutron star can be up to 1014 G (Duncan & Thompson 1992; Paczyński 1992; Usov 1992; Thompson & Duncan 1995, 1996; Vasisht & Gotthelf 1997). The Landau–Lifshitz radiation reaction has been investigated in detail in Landau & Lifshitz (1975) in flat space, while, in curved space, the radiation reaction is described in Sokolov et al. (1978, 1983), DeWitt & Brehme (1960), and Tursunov et al. (2018). Thus, the radiation reaction force can affect the charged particles from the accretion disk around the black hole and the neutron star significantly. Radiation reactions have been an important element of pair creation scenarios in positron–electron plasma just above the pole of the event horizon (Hirotani & Pu 2016). Curvature radiation in black hole magnetosphere pair creation schemes is radiation resistance limited (Broderick & Tchekhovskoy 2015). Recently, a radiation reaction has also been applied to protonic acceleration in the vortex above the pole of the black hole (Ruffini et al. 2018). Radiation reactions are of fundamental importance in the evacuated vortex of black hole magnetospheres (Punsly 2001). In particular, if the field line angular velocity is set much less than the horizon angular velocity by distant plasma and a very tenuous plasma exists in the event horizon magnetosphere, then radiation resistance will determine the flow dynamics of accretion as well as the rotational energy extraction by a putative jet (Punsly 2001, 1991). The dynamics mentioned above cannot be revealed by MHD simulations with mass floors. The ad hoc injection of mass will damp any large waves that can break ideal MHD and prevent the associated large local electromagnetic forces from being achieved. Therefore, all existing numerical simulations of the black hole magnetosphere bypass the radiation reaction dominated dynamics as a consequence of numerical dissipation of waves and numerical diffusion in the MHD system in the evacuated vortex above the event horizon. Thus, a proper treatment of radiation reaction is critical for assessing the time evolution of these types of astrophysical systems. Numerical simulations using general relativistic magnetohydrodynamics (GRMHDs) are applied to investigate the physical process in accretion disks around neutron stars, as well as in microquasars (μQSOs), gamma-ray bursts, and active galactic nuclei. In curved spacetime, the dynamical evolution of the high energy disks made of ion–electron plasma in simulated GRMHD is usually performed without the contribution from Landau–Lifshitz radiation reaction force, which may play a crucial role. The method of particle-in-cell (Chen & Beloborodov 2014; Philippov & Spitkovsky 2014; Belyaev 2015; Cerutti et al. 2015, 2016; Philippov et al. 2015b) containing the radiation reaction forces has been investigated in flat space, while, in curved space, the same method with a radiation reaction has been achieved in Philippov et al. (2015a). Incorporating the radiation reaction into the relativistic magnetohydrodynamic equations governing the dynamics of plasma has been studied by Tam & Kiang (1979) and Berezhiani et al. (2004, 2008). In a recent study, Liu et al. (2018) achieved the one-fluid relativistic magnetohydrodynamics description of two-fluid plasma in which the Landau–Lifshitz radiation reaction is incorporated. However, numerical simulation with the radiation reaction from Liu et al. (2018) is not practical due to its highly nonlinear form at present, we expect that analytical investigations are essential and provide more motivation for future work. Thus, in this work, similar to Liu et al. (2018), we get the GRMHD equation for the one-fluid description of two-fluid plasma containing a Landau–Lifshitz radiation reaction in curved space, and these results could be applied to both positron–electron and proton–electron plasma.

2020MNRAS.496.4127V__Homan_et_al._2010_Instance_1

The recent report by Buisson et al. (2020b) of the detection of Type-I bursts in Sw J1858 shows the presence of a neutron star primary. Therefore, here, we first consider whether Eddington-limited accretion in such a neutron star LMXB might explain the observed radio behaviour in Sw J1858. The neutron star LMXBs with the highest mass accretion rates are the Z-sources, which are thought to accrete near or at the Eddington luminosity, tracing out Z-shaped tracks in their X-ray colour–colour diagrams (Hasinger & van der Klis 1989; Homan et al. 2010). Z-sources can show strong changes in radio brightness, related to the branch they are positioned on in their colour–colour diagram track; they are radio brighter and more variable in the Horizontal Branch than in the Flaring and Normal Branches (Penninx et al. 1988; Hjellming et al. 1990a,b; Spencer et al. 2013; Motta & Fender 2019). Time-resolved radio studies of the different branches in Sco X-1 by Hjellming et al. (1990b) and Cyg X-2 by Hjellming et al. (1990a) show that these sources have similar levels of radio variability and luminosity to Sw J1858 during their radio-faint Flaring and lower Normal Branches. However, in the X-ray – radio luminosity plane, these sources are located to the right of Sw J1858, around the neutron star Eddington limit of 2 × 1038 erg s−1. For Sw J1858 to be similar to these sources, it would therefore have to be viewed a high inclination, reducing its observed X-ray flux and masking the Z-source variability properties through obscuration. However, a clear radio difference between Sw J1858 and the Z-sources is the radio spectral index: contrary to Sw J1858, Z-sources typically show steep spectra and are indeed associated with the launch of (resolved) discrete ejecta (Motta & Fender 2019). Also, in this scenario, Sw J1858 should not have resided in the much more radio bright Horizontal Branch during any of the observations, which is unlikely given the time-scales and commonness of transitions between the branches (Homan et al. 2010).

2017MNRAS.465..383R__Tong_et_al._2013_Instance_1

In general, the Hall time-scale for magnetic field evolution depends on the strength of the magnetic field, as seen in equation (4). For young NSs with fields below 1014 G, this time-scale may be longer than the observed SNR age. Therefore, magnetic field growth does not have a dramatic effect on these young NSs. However, many of these systems are observed to have a braking index n 3 (Espinoza 2012; Archibald et al. 2016). A possible explanation for these low braking indices may be through the emission of a relativistic particle wind (Thompson & Blaes 1998; Harding, Contopoulos & Kazanas 1999; Tong et al. 2013). However, the conclusive detection of wind nebulae around magnetars in particular is challenging due to the presence of dust-scattering haloes that accompany these X-ray bright, heavily absorbed objects (Esposito et al. 2013; Safi-Harb 2013). Only a handful of such nebulae have been proposed to be associated with highly magnetized NSs. For example, a wind nebula has been proposed to surround the magnetar Swift J1834.9–0846 in W41 (Younes et al. 2016), and the luminosity of a particle wind was estimated for SGR 1806–20 based on the X-ray and radio observations of the wind-powered nebula G10.0–0.3 (Thompson & Duncan 1996; Marsden, Rothschild & Lingenfelter 1999; Gaensler et al. 2005). In the pulsar wind model, relativistic particles load the magnetosphere with charge and distort the dipole field at large scales outside of the light cylinder. Besides affecting the NS spin-down, the emission of a relativistic wind can also offer an explanation for the significant timing noise that generally affects magnetar observations (Tsang & Konstantinos 2013). The HBPs J1119–6127 and J1846–0258 are clearly associated with pulsar wind nebulae (Gavriil et al. 2008; Kumar & Safi-Harb 2008; Ng et al. 2008; Safi-Harb & Kumar 2008; Safi-Harb 2013), suggesting that particle wind emission should play an important role in their evolution. We also expect that AXP and SGR evolution may be affected by wind emission due to candidate wind nebulae, but do not consider these models for the CCOs that do not show any evidence of PWN. However, we note that braking exclusively due to a steady particle wind produces a torque with a braking index n = 1, too low for the NSs with secure SNR associations in Table 1.

2017MNRAS.470.2959K___2013_Instance_1

At large distances, the proper motions of the halo stars are either unreliable or generally unavailable, which hinders a direct measurement of their velocity dispersions. However, our off-centric location in the Galaxy means that the galactocentric radial (r) and heliocentric radial (s) directions are not the same. This difference is more significant in the inner halo, at a distance of r ≲ a couple of times of R0, where R0 is the distance of the Sun from the Galactic Centre. Hence, in the inner halo the observed line-of-sight velocities of the stars can be expressed in terms of all three orthogonal galactocentric velocities (vr, vθ, vϕ), or in other words the line-of-sight velocities have some contribution from the tangential galactocentric velocities. Provided we have a model that well represents the distribution of the halo stars, we can fit a model marginalized over the unknown tangential motions to the available four-dimensional data (position vector and a line-of-sight velocity), and thus estimate the velocity moments of the system. In the absence of proper motion, the approach of estimating moments of the velocities has been extensively used to predict the kinematics of the MW halo. For example, Sirko et al. (2004), Kafle et al. (2012), Kafle et al. (2013), Kafle et al. (2014) and King et al. (2015) fit an ellipsoidal distribution of velocities and similarly, Deason, Belokurov & Evans (2011a) apply an alternative power-law model to derive the halo kinematics. Using the marginalisation scheme, Kafle et al. (2012, 2013) studied halo Blue Horizontal Branch stars (BHBs), Kafle et al. (2014) studied both BHBs and K-Giant stars (KGs), while King et al. (2015) analysed a mixed bag of BHB and F-type stars to cumulatively construct the velocity dispersion and anisotropy of the outer halo. Interestingly, Kafle et al. (2012) found that the velocity anisotropy parameter of the Galactic halo is non-monotonic and has a prominent dip at a galactocentric radius of r ≃ 18 kpc. In their studies, King et al. (2015) find that the value of β at 15 ≲ R/kpc ≲ 25 is more tangentially biased, which they attribute to the difference in the spatial resolutions of the data sets and adopt a marginalization technique. A varying level of undulations in the anisotropy parameter has also been observed in simulated haloes (Rashkov et al. 2013; Loebman et al. 2017). There are a number of proposed scenarios that could explain such a feature, e.g. a transition from inner to outer halo or a local shell-like structure at the given radius. Moreover, it can also be due to the unrelaxed stars dispersed from the kinematically coherent satellite galaxies that are aligned with kinematically coherent planar structures; assuming that such planar structures have strong rotation as suggested by Ibata et al. (2013), Pawlowski, McGaugh & Jerjen (2015), Libeskind et al. (2015) and Ibata et al. (2015), etc. Recently, Loebman et al. (2017) suggest that a major merger as early as redshift z ∼ 1 can also result in a tangential dip in the value of β over a wide range of radii. While Bird & Flynn (2015) suggest that such a feature in the velocity anisotropy run of the halo is a transitory phase, Loebman et al. (2017) conclude that such dips are long-lived in the in situ stellar halo. In any case, there is currently no consensus view as to what causes such velocity anisotropy changes. Finally, in the outer halo there have been recent attempts to utilize multi-epoch Hubble Space Telescope data to estimate the halo velocity dispersion. In particular, recently Cunningham et al. (2016) used the Galactic foreground stars along the M31 galaxy and found that the halo is isotropic at r/kpc ∼ 25. In Fig. 10, we summarize the recent (this paper inclusive) measurements of the halo velocity anisotropy.

2021AandA...645A..95H__Boese_2000_Instance_2

Next we chose an optimum cut-off radius for the detector FOV. The PSPC has a circular FOV with a radius 57′. The PSPC entrance window has a rib support structure with an inner ring at a radius corresponding to 20′ (Pfeffermann et al. 1987; Hasinger & Zamorani 2000). Both the ROSAT telescope angular resolution and its vignetting function are roughly constant within the inner 20′ ring, but degrade significantly towards larger off-axis angles. The combined detector and telescope PSFs are described in detail in Boese (2000). To the first order, the PSF at each off-axis angle can be approximated by a Gaussian function with a half power radius (HPR) of 13, 22, 52, 93, 130, and 180″, at off-axis angles of 0, 12, 24, 36, 48, and 57′, respectively (at 1 keV). The vignetting function at 1 keV drops almost linearly to about 50% at an off-axis angle of 50′. Taking into account all these effects, the HPR of the overall RASS PSF is 84″ (Boese 2000). This means that the classical confusion limit (40 beams per source) is reached at a source density of about 15 sources deg−2, which is exceeded in the high-exposure areas of our survey. In addition, we need to optimally discriminate between extended and point-like X-ray sources, calling for an angular resolution that is as high as possible. We therefore have to reduce the detector FOV. The sharpest imaging is achieved within the inner 20′ of the PSPC FOV, corresponding to the inner ring-like rib of the PSPC support structure (see Fig. 1). However, there is a trade-off between image sharpness and the number of photons required for detection and image characterization. In particular in the outer areas of our survey, where the RASS exposure times drop significantly, a 20′ FOV radius does not provide sufficient exposure time. Taking into account the various competing factors in this trade-off, we made a few tests varying the FOV cut-off radius, and finally decided on an optimum FOV radius of 30′. The PSPC detector coordinates have a pixel size of 0.934″. We thus removed all X-ray events from the dataset, which are further than 1925 pixels from the PSPC centre pixel coordinate [4119,3929]. A similar cut had to be applied to the modified PSPC instrument map (MOIMP), which is used later for the construction of the survey exposure map.

2021MNRAS.506.3313G__Gao_&_Ho_2017_Instance_1

The B/T ratio of galaxies has been traditionally measured by modelling their surface brightness profile. Such modelling allows one to find the light-profiles of individual photometric components of galaxies and then measure their properties such as luminosity, shape, size etc. The earliest such works aimed at photometric decomposition of galaxies involved fitting for the azimuthally averaged 1D surface brightness profile (Kent 1985). However, this method leads to systematic errors as creation of 1D light profile by either averaging over the galaxy image or by taking a single cut along the major axis do not properly take into account non-axisymmetric features like a bar or isophotal twist (for more details see Gao & Ho 2017). Due to these shortcomings, techniques to fit the whole 2D image of the galaxy, pixel-by-pixel, were developed (Wadadekar, Robbason & Kembhavi 1999). There are now a number of implementations of 2D bulge disc decomposition available – gim2d: Simard (2010), galfit: Peng et al. (2002), imfit: Erwin (2015), profit: Robotham et al. (2016), budda: de Souza, Gadotti & dos Anjos (2004), gasp2d: Méndez-Abreu et al. (2008) – which fit the 2D images of galaxies. They usually employ different algorithms and methods to find the best-fitting model for a galaxy image. In the last two decades, some pipeline codes have been developed (pymorph: Vikram et al. (2010), galapagos: Barden et al. (2012)) to carry out bulge disc decomposition for large galaxy samples, in a mostly automated fashion. This 2D bulge-disc decomposition approach, while being powerful, is time consuming. The employed fitting algorithm and code carries out a search in parameter space defined by various model parameters and then finds the best fit. Most of the time, one also needs to carry out additional steps (masking other objects in the field and providing PSF images) before the actual fitting of galaxies can commence. Thus estimating the B/T ratio for a single galaxy involves a significant amount of time and computational cost. More importantly, the fitting procedure scales only linearly with the size of the galaxy sample. This limitation will be a major problem in the era of next generation sky surveys (e.g. LSST, Euclid sky surveys) which will observe several billion galaxies.

2021MNRAS.500.1772N__Shibata,_Kiuchi_&_Sekiguchi_2017_Instance_1

While these early studies demonstrated the viability of neutron star mergers as a major r-process site, they identified only one ejection channel: ‘dynamical ejecta’ that are tidally flung out by gravitational torques. Since they are never substantially heated, these ejecta carry their original β −equilibrium electron fraction from the original neutron star, Ye ≈ 0.05, and this enormous neutron-richness allows them to undergo a ‘fission cycling’ process (Goriely, Bauswein & Janka 2011; Korobkin et al. 2012), which produces a very robust r-process abundance distribution close to the solar pattern for A ≥ 130, but hardly any lighter r-process elements. Oechslin, Janka & Marek (2007) pointed out that there is a second channel of mass ejection that also happens on a dynamical time-scale: shock-heated matter from the interface where the stars come into contact. As of today, many more mass ejection channels have been discussed: matter that becomes unbound on secular time-scales (∼1 s) from the post-merger accretion torus (Beloborodov 2008; Metzger, Piro & Quataert 2008; Fernandez & Metzger 2013; Fernandez et al. 2015; Just et al. 2015; Siegel & Metzger 2017, 2018; Fernandez et al. 2019; Miller et al. 2019a), as MHD-driven winds (Siegel & Ciolfi 2015) and by viscous effects (Shibata, Kiuchi & Sekiguchi 2017; Radice et al. 2018a; Shibata & Hotokezaka 2019) from a long-lived neutron star merger remnant. Similar to the case of proto-neutron stars, the enormous neutrino luminosities (>1053 erg s−1) after a neutron star merger can also drive substantial matter outflows (Ruffert et al. 1997; Rosswog & Ramirez-Ruiz 2002; Dessart et al. 2009; Perego et al. 2014; Martin et al. 2015; Radice et al. 2018b). The secular torus ejecta contain approximately 40 per cent of the initial torus mass and, although the latter may vary substantially from case to case, they likely contribute the lion’s share to the total ejecta mass. Due to their different thermal histories and exposure times to neutrinos, the ejecta channels can have different electron fractions Ye and therefore different nucleosynthesis yields.1 For electron fractions below a critical value, $Y_{\rm e}^{\rm crit}\approx 0.25$ (Korobkin et al. 2012; Lippuner & Roberts 2015), lanthanides and actinides are efficiently produced, which, due to their open f-shells, have particularly high bound–bound opacities (Barnes & Kasen 2013; Kasen, Badnell & Barnes 2013; Tanaka & Hotokezaka 2013; Tanaka et al. 2020) and therefore lead to red transients that peak days after the merger. Ejecta with electron fractions above $Y_{\rm e}^{\rm crit}$, in contrast, only produce ‘lighter’ elements with lower opacities and thus result in bluer transients that peak after about 1 d. Opaque, low-Ye ejecta blocking the view on high-Ye ejecta can lead to a ‘lanthanide curtaining’ effect (Kasen, Fernández & Metzger 2015; Wollaeger et al. 2018), which will efficiently block blue light. Therefore, it is important to understand the layering, dynamics, interaction and potential mixing of different ejecta channels.

2019AandA...623A..75V__Tetarenko_et_al._2018a_Instance_1

The X-ray transient MAXI J1820+070 was first detected on 2018 March 11 (Kawamuro et al. 2018) by the Monitor of All-sky X-ray Image (MAXI, Matsuoka et al. 2009) and was associated with the optical transient ASASSN-18ey (Denisenko 2018; Tucker et al. 2018). In the X-rays, the source flux exceeded 3 Crabs (Bozzo et al. 2018; Mereminskiy et al. 2018), and in the optical, the source reached a magnitude of mV = 12 − 13 (Littlefield 2018; Russell et al. 2018). The parallax of the source π = 0.3 ± 0.1 mas was presented in the Gaia DR2 catalogue (Gaia Collaboration 2018). This corresponds to a distance of 3 . 9 − 1.3 + 3.3 $ 3.9^{+3.3}_{-1.3} $ kpc (Gandhi et al. 2018a). This unusually bright event allows a detailed investigation of multi-wavelength spectral and timing properties. The course of the outburst was monitored in radio (Trushkin et al. 2018; Polisensky et al. 2018), sub-millimeter (Tetarenko et al. 2018a), optical (Baglio et al. 2018), X-rays (Uttley et al. 2018), and γ-rays (Bozzo et al. 2018; Kuulkers et al. 2018). Because the object was sufficiently bright even for small telescopes, the target was almost continuously monitored, and a rich variety of phenomena was observed. Fast variability and powerful flares in the optical and infrared (Littlefield 2018; Sako et al. 2018; Gandhi et al. 2018b; Casella et al. 2018), optical, and X-ray quasi-periodic oscillations (Mereminskiy et al. 2018; Yu et al. 2018a,b; Buisson et al. 2018; Zampieri et al. 2018) as well as low linear polarisation (Berdyugin et al. 2018) were detected in the source. A 17 h photometric period was recently reported (Patterson et al. 2018) and was tentatively associated with the orbital or superhump period (previously, the source showed a 3.4 h periodicity, Richmond 2018). The X-ray spectral and timing properties as well as the optical-to-X-ray flux ratio suggests that the source is a black hole binary (Baglio et al. 2018; Mereminskiy et al. 2018).

2019MNRAS.490.5739X__Zahn_et_al._2011_Instance_1

It is generally believed that reionization started first in high-density regions, where the first luminous objects formed first. In the ‘bubble model’ of reionization (Furlanetto, Zaldarriaga & Hernquist 2004), the amount of star formation and the resulting ionizing photons are estimated using the excursion set model. Based on this idea, the so-called ‘seminumerical simulations’ have been developed. For example, Mesinger, Furlanetto & Cen (2011) developed the 21cmFAST,1 to simulate the evolution of the 3D density, ionization, and 21cm brightness temperature fields efficiently. The ‘bubble model’ considers spherical regions of increasingly smaller scales and identifies ionized bubbles by comparing the cumulative number of ionizing photons produced within the region with the number consumed in reionization process. It has been demonstrated that the statistical predictions of the ‘bubble model’ and the 21cmFAST agree fairly well with radiative–hydrodynamic simulations (Zahn et al. 2007; Mesinger et al. 2011; Zahn et al. 2011), at least when recombination, feedback, etc. are ignored. However, during most epochs of reionization, the topology of the ionization field is much more complicated than the isolated bubbles configuration. The bubbles start to connect with each other as early as when the global ionized fraction is just about 10 per cent, and the Universe starts the percolation process when the ionized fraction gets ∼30 per cent (see e.g. Furlanetto & Oh 2016; Chen et al. 2018). Inspired by the bubble model, the 21cmFAST determines the ionization state of each point by comparing the expected ionizing photon production in the surrounding region with the required number, but it allows non-spherical geometry for the ionized regions. In order to give a better description of the evolution of neutral regions after percolation, Xu et al. (2014) developed the so-called ‘island model’, assuming isolated neutral islands topology. The island model also takes into account an ionizing background that is inevitable during the late EoR (Furlanetto & Oh 2005; McQuinn, Oh & Faucher-Giguère 2011; Emberson, Thomas & Alvarez 2013). Based on the ‘island model’, a seminumerical code named islandFAST was developed to mimic the islands evolution during the last stage of reionization (Xu et al. 2017). In the islandFAST, the effect of small-scale absorbers is taken into account empirically by adopting a fitting formula for the evolution of mean free path (MFP) of ionizing photons (Songaila & Cowie 2010), based on the observed number density of Lyman limit systems up to redshift 6. Before the completion of reionization, the MFP is limited by both the underdense islands and the overdense absorbers. The evolution of the ionization field and the intensity of the ionizing background are derived self-consistently by an iterative procedure to ensure convergence in the total effective MFP of the ionizing photons.

2021AandA...655A.111K__Bovy_et_al._2012_Instance_1

Over the last decade, the radial and vertical dependences of the metallicity-alpha-element distribution have been studied in more and more detail with increasingly larger samples (e.g., Bensby et al. 2011; Anders et al. 2014; Nidever et al. 2014; Hayden et al. 2015; Queiroz et al. 2020). Figure 6 is mostly consistent with similar plots shown in the above papers. In the inner 10 kpc, it displays two over-densities, a high alpha-element (here [Mg/Fe]), and a low one. Between Rg = 6 and 10 kpc, the two over-densities define two different sequences. In Appendix E, we note that when the sample is restricted to a ±500 pc layer around the Galactic plane, two close but separated sequences are observed in the Rg ∈ [4, 6] kpc interval. Because of their scale height (Bovy et al. 2012), kinematics (Bensby et al. 2003), and age properties (Haywood et al. 2013), these two sequences are associated with the thick disc (high-alpha) and thin disc (low-alpha), respectively. Moving inward of Rg = 4 − 6 kpc, Fig. 6 shows that the two over-densities connect through a zone of lower density to form a single sequence. This is in agreement with the observations of Hayden et al. (2015), Bensby et al. (2017), Zasowski et al. (2019), Bovy et al. (2019), and Lian et al. (2020a,b), who also report a single sequence in the inner disc and/or in the bulge/bar area. Conversely, Rojas-Arriagada et al. (2019) and Queiroz et al. (2020) observe two sequences in the inner regions. In Appendix F, we compare the distributions of different APOGEE DR16 alpha elements in the ([Fe/H], [α/Fe]) plane (restricting the sample to the stars contained in the Rg ∈ [0, 2] kpc interval). The different elements produce different patterns: the global alpha-element abundance5 and oxygen show a double sequence, while magnesium, silicon, and calcium present a single sequence. This could explain, at least partly, why Queiroz et al. (2020), who use a combined α-element abundance, observe a double sequence, while we see a single one with magnesium. However, this does not explain the discrepancy with Rojas-Arriagada et al. (2019), who also used magnesium. Beyond Rg = 10 kpc, the high-alpha sequence gradually vanishes. This is in agreement with the finding that the thick disc has a shorter scale length than the thin disc (Bensby et al. 2011; Cheng et al. 2012; Bovy et al. 2012). It should be emphasised that in this paragraph the term ‘sequence’ is used in the geometrical sense. It does not presuppose the number of chemical tracks that form the sequence or sequences. In particular, based on Fig. 6, it can not be excluded that the single geometrical sequence observed in the inner disc be made of two chemical tracks, with the low-alpha one restricted to a narrow metallicity range. We discuss and propose an interpretation of the inner disc sequence in Sect. 5.