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2021MNRAS.501.4035R__Vigren_&_Galand_2013_Instance_1

The study of cometary plasma composition has been subjected to a great interest after the ion mass spectrometer onboard Giotto spacecraft detected many peaks in the mass range 12 and 120 amu (Balsiger et al. 1986; Krankowsky et al. 1986; Mitchell et al. 1987; Altwegg et al. 1993). By developing photochemical models, numerous studies focused on comet 1P/Halley explained the observed ion distribution in a water-dominated coma (Allen et al. 1987; Wegmann et al. 1987; Schmidt et al. 1988; Cravens 1989; Bhardwaj, Haider & Singhal 1990, 1996; Gan & Cravens 1990; Ip et al. 1990; Haider, Bhardwaj & Singhal 1993; Häberli et al. 1995; Haider & Bhardwaj 1997, 2005; Bhardwaj 1999; Rubin et al. 2009; Cordiner & Charnley 2014). By making 2 yr of observations, the recent Rosetta space mission on comet 67P/Churyumov–Gerasimenko has revolutionized our understanding of the activity of the cometary coma. During the Rosetta observation period, continuous measurements around the nucleus were helpful to study the evolution of ion and neutral distribution and also the driving photochemical processes in the coma. Several modelling works on this comet have shown that ion composition in the coma varies based on the sublimation rate of the nucleus (Vigren & Galand 2013; Fuselier et al. 2015, 2016; Galand et al. 2016; Heritier et al. 2017, 2018; Vigren et al. 2017; Beth, Galand & Heritier 2019). All these studies show that solar photons are the primary energy source that determines the ion composition in the inner coma. Solar extreme ultraviolet photons having an energy more than 12 eV ionize H2O and produce H2O+, and the collisions among these species quickly lead to the formation of H3O+. The sublimated parent species such as CH3OH, NH3, HCN, HCOOH, and CH3CHO, have high proton affinities compared to that of H2O, causing the loss of H3O+ in the inner coma. Haider & Bhardwaj (2005) developed a comprehensive chemical network to study the ion distribution in comet 1P/Halley. Their calculations show that NH$_4^+$ is the most dominant ion in the inner coma followed by H3O+ and CH3OH$_2^+$ ions. Similarly, the model calculations of Heritier et al. (2017) on comet 67P/Churyumov–Gerasimenko showed that NH$_4^+$, CH3OH$_2^+$, H3O+, H3S+, and HCNH+ are the important ions in the inner coma. They also showed that the densities of these ions vary with the relative mixing ratios of corresponding proton affinity species coming from the nucleus. Even if the mixing ratios of parent species, which have high proton affinity, are very low (2 per cent), they can play a significant role in modifying the ionospheric composition of the inner coma. Hence, the ion distribution in the cometary coma essentially depends on the neutral composition and photochemical reactions.

2022MNRAS.512.2222V__Granato_et_al._2004_Instance_1

As for the ETGs, which are spheroid-dominated, most of these that are more massive than ∼2 × 1010 M⊙ are old, implying that the SF has strongly declined long time ago and no recent bursts of SF have occurred. Early formation and rapid quenching mechanisms are expected for those galaxies, where the quenching mechanisms are likely to be associated with their morphological transformation through wet major mergers and disc instabilities. These processes lead to compaction and strong bursts of SF that consume the gas (e.g. Hopkins et al. 2008; Barro et al. 2013; Dekel & Burkert 2014), as well as strong AGN/QSO and Supernova feedback that will heat and expel the gas (e.g. Granato et al. 2004; Sijacki et al. 2007; Somerville et al. 2008; Vogelsberger et al. 2014). At intermediate and low masses, the environment-driven quenching mechanisms (e.g. ram-pressure and tidal striping, strangulation, etc.) can also be relevant (e.g. Kauffmann et al. 2004; Peng et al. 2010; Schawinski et al. 2014). Note that these mechanisms are expected to lead also to morphological transformation (e.g. Gunn & Gott 1972; Moore et al. 1996; Abadi, Moore & Bower 1999; Bekki, Couch & Shioya 2002; Aragón-Salamanca, Bedregal & Merrifield 2006). However, as M* is lower there is an increasing fraction of ETGs with intermediate values of Agelw, or even low values (Table 3), though not as low as in the case of low-mass LTGs (see Fig. 16). This population of ETGs is partially associated to the Blue Star-forming and Recently Quenched Early-type galaxies, for which rejuvenation processes (Thomas et al. 2010) by gas infall or late gas-rich mergers were proposed in Lacerna et al. (2016, 2020). In fact, the $Age_{\rm mw}/{Age_{\rm lw}}$ ratio of these galaxies is large, which implies that they did not form so late but they contain some (small) fractions of very young stellar populations. Finally, for the ITGs, the Agelw distribution in the ∼0.5–5 × 1010 M⊙ mass range is roughly bimodal (Fig. 16), with a large fraction of them in the intermediate age region (or green valley in the colour–M* diagram), showing that the quenching time-scales for these galaxies with a significant bulge are slow (quenching mechanisms like those driven by morphology, halo mass, and environment; see above). For the most massive S0–Sa galaxies, ${Age_{\rm lw}}\gt 4$ Gyr, that is, they are mostly quenched, while for the lest massive, ${Age_{\rm lw}}\lesssim 2$ Gyr, which imply some rejuvenation processes.

2016AandA...595A..72M__Vergani_et_al._2015_Instance_1

On the other hand, the Australia Telescope Compact Array (ATCA) 21 cm line survey of GRB host galaxies revealed high levels of atomic hydrogen (H i), suggesting that the connection between atomic gas and star formation is stronger than previously thought (Michałowski et al. 2015). Star formation may be directly fuelled by atomic gas, as has been theoretically shown to be possible (Glover & Clark 2012; Krumholz 2012; Hu et al. 2016), and this is supported by the existence of H i-dominated, star-forming regions in other galaxies (Bigiel et al. 2008, 2010; Fumagalli & Gavazzi 2008; Elmegreen et al. 2016). This can happen in a low metallicity gas that is recently acquired, even if the metallicity in other parts of a galaxy is higher, near the onset of star formation because cooling of gas (necessary for star formation) is faster than the H i-to-H2 conversion (Krumholz 2012). Indeed, large atomic gas reservoirs, together with low molecular gas masses (Hatsukade et al. 2014; Stanway et al. 2015b) and stellar masses (Perley et al. 2013, 2015; Vergani et al. 2015), indicate that GRB hosts are preferentially galaxies that have very recently started a star formation episode. This provides a natural route for forming GRBs in low-metallicity environments, as found for most GRB hosts (Fruchter et al. 2006; Modjaz et al. 2008; Levesque et al. 2010a; Han et al. 2010; Boissier et al. 2013; Schulze et al. 2015; Vergani et al. 2015; Japelj et al. 2016; Perley et al. 2016), except of a few examples of hosts with solar or super-solar metallicities (Prochaska et al. 2009; Levesque et al. 2010b; Krühler et al. 2012; Savaglio et al. 2012; Elliott et al. 2013; Schulze et al. 2014; Hashimoto et al. 2015; Schady et al. 2015; Stanway et al. 2015a). Indeed, the GRB collapsar model requires that most of the GRB progenitors have low metallicity (below solar) in order to reduce the loss of mass and angular momentum that is required for launching the jet (Yoon & Langer 2005; Yoon et al. 2006; Woosley & Heger 2006). We note however that other models, while still predicting the metallicity preference (e.g. Izzard et al. 2004; Podsiadlowski et al. 2004; Detmers et al. 2008), allow higher metallicities owing to differential rotation (Georgy et al. 2012), binary evolution (Podsiadlowski et al. 2010; van den Heuvel & Portegies Zwart 2013), or weaker magnetic fields (Petrovic et al. 2005).

2015AandA...582A..22L__Todorov_et_al._2014_Instance_2

Dust distribution:We employed the standard flared disk model with well-mixed gas and dust, which has been successfully used to explain the observed SEDs of a large sample of young stellar objects and BDs (e.g., Wolf et al. 2003; Sauter et al. 2009; Harvey et al. 2012a; Joergens et al. 2013; Liu et al. 2015). The structure of the dust density is assumed with a Gaussian vertical profile (1)\begin{equation} \rho_{\rm{dust}}=\rho_{0}\left(\frac{R_{*}}{\varpi}\right)^{\alpha}\exp\left[-\frac{1}{2}\left(\frac{z}{h(\varpi)}\right)^2\right], \label{dust_density} \end{equation}ρdust=ρ0R∗ϖαexp−12zh(ϖ)2,and the surface density is described as a power-law function (2)\begin{equation} \Sigma(\varpi)=\Sigma_{0}\left(\frac{R_{*}}{\varpi}\right)^p, \end{equation}Σ(ϖ)=Σ0R∗ϖp,where ϖ is the radial distance from the central star measured in the disk midplane, and h(ϖ) is the scale height of the disk. The disk extends from an inner radius Rin to an outer radius Rout. To the best of our knowledge, among our sample, there are five objects that have been identified as binary systems so far. They are 2M1207 (a~55 AU, Chauvin et al. 2004), J04221332+1934392 (a~7 AU, Todorov et al. 2014), J04414489+2301513 (a~15 AU, Todorov et al. 2014), USD161833 (a~134 AU, Bouy et al. 2006), and USD161939 (a ~ 26 AU, Bouy et al. 2006), where a refers to the separation within the system. The disks around individual components in binary systems are expected to have truncation radii of the order of (0.3 − 0.5)a (Papaloizou & Pringle 1977). We adopted 0.5 a as the disk outer radii for 2M1207, USD161833, and USD161939. For the close pairs (a ≲ 15 AU, J04221332+1934392 and J04414489+2301513), dynamical simulations of star-disk interactions suggest that individual disks are unlikely to survive (e.g., Artymowicz & Lubow 1994). Disk modeling is complicated in those close multiple systems. For simplicity, we assume that the emission is associated with circumbinary disks of 100 AU in size. For other objects, we fix Rout = 100 AU in the modeling because the choice of this parameter value makes essentially no difference to the synthetic SEDs in the simulated wavelength range (Harvey et al. 2012a). The scale height follows the power-law distribution(3)\begin{equation} h(\varpi) = H_{100}\left(\frac{\varpi}{100\,\rm{AU}}\right)^\beta,\\ \end{equation}h(ϖ)=H100ϖ100 AUβ,with the exponent β characterizing the degree of flaring and H100 representing the scale height at a distance of 100 AU from the central star. The indices α, p, and β are codependent through p = α − β. We fix p = 1, which is the typical value found for T Tauri disks in the sub-millimeter (e.g., Isella et al. 2009; Guilloteau et al. 2011), since only spatially resolved data can place constraints on this parameter (e.g., Ricci et al. 2013, 2014). Dust properties:We assume the dust grains to be a homogeneous mixture of 75% amorphous silicate and 25% carbon with a mean density of ρgrain = 2.5 g cm-3 and the complex refractive indices given by Jäger et al. (1994, 1998), and Dorschner et al. (1995). Porous grains are not considered because the fluxes at wavelengths beyond ~ 2 μm are almost independent of the degree of grain porosity in low-mass disks, as shown by Kirchschlager & Wolf (2014). The grain size distribution is given by the standard power law dn(a) ∝ a-3.5da with minimum and maximum grain sizes amin = 0.1 μm and amax = 100 μm, respectively. The choice of the minimum value for the grain size, amin, ensures that its exact value has a negligible impact on the synthetic SEDs. Since there is no information about the maximum grain sizes of our target disks, as provided, e.g., by the (sub)millimeter spectral index, we adopt amax = 100 μm. The Herschel/PACS far-IR observations are sensitive to dust grains with this assumed sizes. Strong grain growth up to millimeter sizes as detected in some BD disks (e.g., Ricci et al. 2012, 2013, 2014; Broekhoven-Fiene et al. 2014) would remain undetected in our data and could affect the disk mass. Our prescription for the dust properties is identical to those used in Liu et al. (2015).

2019ApJ...882..144K__Berk_et_al._2001_Instance_2

The FOCAS and NIRSPEC spectra of PSO J006+39 were obtained at two different epochs separated by 1 yr and 9 months (by slightly less than 3 months in the quasar rest frame). Previously, we found that the PS1 y-band light curve of PSO J006+39 shows brightness variations with a peak-to-peak amplitude of ∼0.7 mag over ∼4 yr (Koptelova et al. 2017), which might be due to the flux variations of both continuum and Lyα line of PSO J006+39. To infer the brightness state of PSO J006+39 at the epochs of its FOCAS and NIRSPEC observations, we first calculated the spectral slope of the quasar continuum from the NIRSPEC spectrum with a wider wavelength coverage than that of the FOCAS spectrum. Using wavelength intervals of 11100–11300, 11400–11600, 13085–13400, and 14700–15200 Å we measured a spectral slope of αλ = −1.35 ± 0.26, where the quoted uncertainty is the statistical error of the fit. The fitted power law is shown in Figure 2 with a solid line. The estimated continuum slope is consistent but somewhat flatter than the typical slope of luminous quasars (Zheng & Malkan 1993; Vanden Berk et al. 2001; Selsing et al. 2016). We then fitted the FOCAS data using the power law with a fixed spectral slope of αλ = −1.35 and spectral windows of 9700–9850 and 10050–10100 Å. The spectral windows adopted for the analysis of the FOCAS and NIRSPEC spectra were taken to be similar to the rest-frame wavelength intervals commonly used to fit the continua of quasars (Vanden Berk et al. 2001; Decarli et al. 2010; Lusso et al. 2015) and less affected by the contribution from emission lines on the red side of the Big Blue Bump (BBB; e.g., Malkan 1983). The estimated continuum flux of PSO J006+39 at the epoch of its FOCAS observations is shown in Figure 2 with a dashed line. By comparing the continuum flux at the epochs of the FOCAS and NIRSPEC observations, we find that the brightness state of PSO J006+39 was different at these two epochs. PSO J006+39 was brighter by about 0.8 mag during the FOCAS observations than during the NIRSPEC observations. Thus, the continuum flux of PSO J006+39 might be different at different epochs depending on the brightness state of the quasar. Figure 2 also shows the fluxes of PSO J006+39 in the FOCAS Y, and NIRSPEC N2, N4, and N6 bands at the epochs of the FOCAS and NIRSPEC observations (see also Table 1).

2017ApJ...835..246Y__Yoon_&_Seough_2014_Instance_1

The present analysis builds upon the macroscopic-kinetic model of the solar wind, originally formulated by Yoon & Seough (2014) for the proton temperatures. The same model was recently generalized to include collisional dissipation (Yoon 2016a, 2016b). The basic methodology is similar to that of Marsch & Tu (2001) and Jasperse et al. (2006), especially in regards to treating the particle aspect, but unlike earlier works (Marsch & Tu 2001; Jasperse et al. 2006), which do not treat the waves self-consistently, Yoon & Seough (2014) and Yoon (2016a, 2016b) discuss the wave generation in a self-consistent manner by solving the adiabatic dispersion relation and wave kinetic equation for each spatial location. We now extend the original formalism (Yoon & Seough 2014) in another direction. We include dynamic electrons, but unlike the two later works (Yoon 2016a, 2016b), we ignore collisional dissipation. For an inhomogeneous plasma immersed in a diverging or converging magnetic field, the kinetic equation for the particles subject to perturbations propagating in parallel direction, s, is given by 1 where, for cylindrical velocity coordinate system, the velocity diffusion coefficient tensor is given by 2 and where is the complex angular frequency, which must be determined by the local dispersion relation, 3 In the above relation, ea and ma are unit electric charge and mass for particles species a (a = p for protons and a = e for electrons, for protons and for electrons); stands for cyclotron frequency for species a, B0 and c being the ambient magnetic field intensity and the speed of light in vacuo; is the square of the plasma frequency defined for species a, n0 being the ambient plasma density, and being the perturbed electric field associated with the unstable transverse mode propagating parallel (and anti-parallel) to the ambient magnetic field, ± denoting the right/left-hand circular polarization. The spectral electric field wave energy density must be determined by solving the wave kinetic equation, the simplest form of which is given by the quasilinear theory, 4

2021MNRAS.503.1319G__Chae_&_Mao_2003_Instance_2

In the first scenario, we assume that neither the characteristic velocity dispersion (σ*) nor the number density (n*) of galaxies evolves with redshifts (νn = νv = 0). Given the redshift coverage of the lensing galaxies in the lens sample (0.06 zl 1.0), if we constrain a non-evolving VDF using the lens data, then, assuming the VDF evolution with redshift is smooth, the fits on the VDF parameters may represent the properties of ETGs at an effective epoch of z ∼ 0.5. Such non-evolving VDF has been extensively applied in the previous studies on lensing statistics (Chae & Mao 2003; Ofek et al. 2003; Capelo & Natarajan 2007; Cao et al. 2012a). By applying the above-mentioned χ2 – minimization procedure to Sample A – we obtain the best-fitting values and corresponding 1σ uncertainties (68.3 per cent confidence level): $\alpha =0.66^{+2.13}_{-0.66}$, $\beta =2.28^{+0.24}_{-0.18}$. It is obvious that the full sample analysis has yielded improved constraints on the high-velocity exponential cut-off index β, compared with the previous analysis of using the distribution of image separations observed in CLASS and PANELS to constrain a model VDF of ETGs (Chae 2005). Suffering from the limited size of lens sample, such analysis (Chae 2005) found that neither of the two VDF parameters (α, β) can be tightly constrained, due to the broad regions in the α − β plane. Consequently, the image separation distribution is consistent with the SDSS measured stellar VDF (Sheth et al. 2003) and the Second Southern Sky Redshift Survey (SSRS2) inferred stellar VDF (Chae & Mao 2003), although the two stellar VDFs are significantly different from each other concerning their corresponding parameter values. We also consider constraints obtained for the Sample B (defined in previous section), with the likelihood is maximized at $\alpha =1.00^{+2.38}_{-1.00}$ and $\beta =2.34^{+0.26}_{-0.24}$, from which one could clearly see the marginal consistency between our fits and recent measurements of three stellar VDFs (especially the SDSS DR5 VDF of ETGs).

2020AandA...639A.107S__Helling_et_al._2019a_Instance_1

Micro-porosity is the porosity arising from the organisation of the condensate monomers (e.g. Mg2 SiO4 in Mg2 SiO4[s]) within a cloud particle during growth. This is different from the porosity that can be used to characterise aggregates that originate from particle-particle collision processes (coagulation, e.g. Dominik & Tielens 1997; Blum & Wurm 2000), which we do not considerhere. On Earth, the material density of water ice is dependent on the ambient temperature at formation. Snowflakes are known to form many types of crystal structures that can be up to 84% porous for millimetre-sized cloud particles when compared toice material density (Hales 2005), leading to the possibility of altitude-dependent porosity in terrestrial snow clouds. Earth-like exoplanets, mini-Neptunes, and T-type brown dwarfs may form water clouds, composed of liquid or solid particles, but warmer planets and brown dwarfs of L-type and later have been shown to form cloud particles made of a mix of materials that is dominated by Mg, Si, Fe, and O and to a lesser extent by Ti, Al, K and other elements (e.g. Witte et al. 2009; Lee et al. 2015; Helling et al. 2019a). There are many ways in which this micro-porosity might be incorporated into mineral cloud particles, for example lattice faults at the interfaces between two different condensation species owing to the different lattice structures. Even for homogeneous growth, single species often have multiple crystal structures (Sood & Gouma 2013), which can also generate lattice faults at their interfaces. For example, the TiO2 [s] rutile and anatase forms are both stable at atmospheric pressures for temperatures greater than 1100 K (Jung & Imaishi 2001; Hanaor & Sorrell 2010). Additionally, within crystal structures, there are many known types of defect that might further decrease material density (e.g. Schottky defects in TiO2 [s] and MgO[s] crystals Ménétrey et al. 2004). Furthermore, these cloud particles not only change their material composition when falling through the atmosphere, but their particle sizes will also change such that the largest cloud particles are forming the innermost part of the cloud, which often sits deep inside the optically thick part of the atmosphere. Because cloud particles made of a mix of many thermally stable materials fall into warmer atmospheric regions, the low-temperature materials (such as SiO[s], MgSiO3[s]) become thermally unstable, they evaporate and leave behind a skeleton made of high-temperature materials (such as Fe[s], TiO2[s], Al2O3[s]). Whilst this may be a source of micro-porosity of cloud particles, Juncher et al. (2017) noted that this may also lead to a reduction in micro-porosity because the structural integrity of the particle is weakened and dangling structures break off. These micro-porous mineral cloud particles we call “mineral snowflakes”.

2018ApJ...852...45W__Ghisellini_et_al._2013_Instance_1

Similarly to former works (e.g., Zhang et al. 2010, 2012; Kang et al. 2014, 2016; Yan et al. 2016), we neglect the low-frequency radio data and consider the data with (or ) in our SED fitting with the one-zone model due to the fact that radio emission should come from the large-scale jet and cannot be accounted for with a one-zone model. The variability correlation between millimeter, optical, X-ray, and γ-ray emission support the fact that they come from more or less the similar region (e.g., Sikora et al. 2008; León-Tavares et al. 2012; Wehrle et al. 2012; D’Ammando et al. 2013; Orienti et al. 2013). In four LSP blazars (0333 + 321, 0430 + 052, 2145 + 067, 2230 + 114), the putative UV excesses are not included in our SED fitting, as they should come from the cold accretion disk (Shakura & Sunyaev 1973; Ghisellini et al. 2013; Ajello et al. 2016). On average, there are 17 data points in our fitting. As an example, we show the multi-wavelength SED and its fitting for 2200 + 420 (BL Lac) in Figure 1 (left panel), where only the SSC process is considered due to the EC not being important in this source. In the right panel of Figure 1, we show the probability distributions of the model parameters, where G, , , , , , and (upper and lower limits represent 1σ errors, see also Table 1). The source of 1219+285 is also a BL Lac object, for which the EC component is negligible. For the remaining 23 LSP sources, both the SSC and EC component are considered, where the SEDs and the fitting are shown in Figures 2–6. For each source, the SED fitting with seed photons from the torus and BLR are presented in left and right panels, respectively. In our SED fitting, we find that most LSP blazars have (20 of 23 sources, for the two other sources and only one has the ratio ∼0.6, see Table 1), where the distribution of the ratio is shown in Figure 7. Our results suggest that the SED fitting with the seed photons from torus are better than with those from the BLR, which supports the notion that the location of the γ-ray emitting region should stay outside of the BLR. In the following work, therefore, we consider the model parameters from the SED fittings with the torus seed photons.

2015AandA...575A.111D__Drimmel_&_Spergel_2001_Instance_1

Using our stellar parameters, we derived an estimate of the spectroscopic distances of XO-2N and XO-2S by means of the following procedure. We generated Monte Carlo (MC) normal distributions for each spectroscopic parameter Teff, [Fe/H], and log  g, composed of 10 000 random values and centred on the best estimates (Table 2). By keeping the stellar radii fixed to the values listed in Table 2 (for XO-2N we used the most accurate estimate), for each MC simulation we first determined the stellar bolometric luminosity L∗ (in solar units) from the Stefan-Boltzmann law, and then we derived the absolute bolometric magnitude Mbol from the relation Mbol = 4.75 − 2.5·log (L∗). By estimating the appropriate bolometric correction (BC), a value for the absolute magnitude in V-band MV was then obtained. The BC term was evaluated using the code provided by Casagrande & VandenBerg (2014). An additional input is the colour excess E(B − V) of the star, which we derived through the relation E(B − V) = AV(s)/3.1, where AV(s) is the interstellar dust extinction in V-band integrated at the distance s of the star (in pc) and measured along the line of sight. We derived AV(s) by adopting a simplified model of the local distribution of the interstellar dust density (Drimmel & Spergel 2001), expressed by the relation ρ = ρ0·sech2(z/hs), where z is the height of the star above the Galactic plane and hs is the scale-height of the dust, for which we adopted the value of 190 pc. The term z is related to the distance s and the Galactic latitude of the star b by the formula z = s·sinb. From this model we obtained the relation AV(s) = AV(tot)·sinh(s·sin b/hs)/cosh (s·sin b/hs), where AV(tot) is the interstellar extinction in V band along the line of sight integrated through the Galaxy, and can be estimated from 2D Galactic maps. For this purpose we used the value AV (tot) = 0.16 mag derived from the maps of Schlafly & Finkbeiner (2011)6. By assuming s = 150 pc as a prior distance of the stars (Burke et al. 2007), we obtained AV (150 pc) ~ 0.06 mag, corresponding to E(B − V) = 0.019 mag. This is the value used as input to the code of Casagrande & VandenBerg (2014) to obtain a first guess of the BC in V-band. This in turn was used in the distance modulus formula V − (Mbol − BCV) = 5·log (s) −5 − AV(s), to obtain a new value for the stellar distance s. The new distance was used to repeat the procedure iteratively, by determining at each step a new value of AV(s) and BCV(s), and finally another estimate of s. When the absolute difference between the last and previous calculated values of s was below 0.1 pc, the iterative process was interrupted and the last derived value for s was assumed as the distance of the star for the Nth Monte Carlo simulation. The adopted estimates for the distance of the XO-2 components are the median of the distributions of the 10 000 MC values, and the asymmetric error bars defined as the 15.85th and 68.3th percentile (see Table 1). Because they are model-dependent, we do not argue here whether the difference of ~2.5 pc between the XO-2S and XO-2S distances is real. We only note that the two values are compatible within the uncertainties and that our best estimate for the distance of XO-2N locates the star a few parsec closer than reported by Burke et al. (2007).

2019ApJ...875...90L__Pontieu_et_al._2011_Instance_1

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

2016ApJ...825..150C__Bermúdez_et_al._2013_Instance_1

The rotational spectrum of NaCl has been obtained using two different FTMW spectrometers constructed at the University of Valladolid. A solid rod was prepared by pressing the NaCl fine powder mixed with a small amount of commercial binder and was placed in the ablation nozzle (Alonso et al. 2009; Mata et al. 2012). A picosecond Nd:YAG laser (12 mJ per pulse, 20 ps pulse width) was used as a vaporization tool. Then, NaCl neutral molecules were supersonically expanded using the flow of a carrier gas (Ne at backing pressure of 15 bars) into the spectrometer chamber. NaCl was first investigated using a chirped-pulse Fourier transform microwave (CP-FTMW) spectrometer with a laser ablation source (Mata et al. 2012; Bermúdez et al. 2013) operating between 6.0 and 12.0 GHz to sample swiftly the rotational spectra of the different species present in the supersonic expansion. Chirped pulses of 4 μs directly generated by the 24 Gs s−1 AWG were amplified to about 300 W peak power using a traveling wave tube amplifier. The amplified pulse is broadcasted into the vacuum chamber through two microwave horns, interacting with the vaporized molecules in the pulsed jet. Finally, a total of 40,000 free induction decays (4 FID emissions per gas pulse), of 10 μs length duration, were averaged and digitized using a 50 Gs s−1 digital oscilloscope. Line widths of the order of 100 kHz FWHM were achieved. The sub-Doppler resolution LA-MB-FTMW spectrometer, described elsewhere (Alonso et al. 2009), operating from 4 to 26 GHz, was used to record the NaCl spectra with the resolution necessary to analyze the hyperfine structure due to the presence of two nuclei with I = 3/2 in the molecule. Microwave pulses of 0.3 μs duration with powers of 140 mW were applied to polarize the molecules in the jet. The free induction decay (FID) was recorded for 100 μs in the time domain at 40–100 ns sample intervals and then converted to the frequency domain by Fourier transformation. All the transitions appeared as Doppler doublets due to the parallel configuration of the molecular beam and the microwave radiation. The resonance frequency was determined as the arithmetic mean of the two Doppler components. The estimated accuracy of the frequency measurements is greater than 3 kHz. From 10 (in the case of the ground state and the lower vibrational states) to 2500 (for the higher vibrational states) averages were phase-coherently co-added to achieve reasonable signal-to-noise ratios.

2019MNRAS.489..855C__Husemann_et_al._2013_Instance_1

The size of ENLRs have been defined in different ways in the literature. Bennert et al. (2002) and Schmitt et al. (2003b) used the Hubble Space Telescope (HST) to obtain narrow band images of $\rm [O\, III]$, and adopted the maximum 3σ detected radius as the radius of the ENLR. This method is subject to the instrumental sensitivity limit that could be very different in different observations. Studies with long-slit spectroscopic observations define the radius based on isophote (Greene et al. 2011; Hainline et al. 2013, 2014), or the distance at which the ionization state changes from AGN to star-forming activities (Bennert et al. 2006a,b). The long-slit based observations also have drawbacks: the morphology of ENLR is sometimes irregular so that the derived size depends on the orientation of slits (Greene et al. 2011; Husemann et al. 2013). We have compared the measured size based on the IFU and the mock long-slit observation in Fig. 4 following the method discussed below. In most cases, long-slit observations tend to underestimate the true size of ENLR. IFU spectroscopic data allow us to use 2D maps to define the sizes of ENLRs. Common definitions include the radius of a specified $\rm [O\, III]$ surface brightness isophote (Liu et al. 2013, 2014), or the $\rm [O\, III]$ flux weighted radius (Husemann et al. 2013, 2014; Bae et al. 2017). We followed the same method as Liu et al. (2013) but chose a different threshold. The isophote threshold of 10−15$\rm erg\, s^{-1}cm^{-2}arcsec^{-2}$ was used for quasars related studies. This is suitable for such bright objects but are not as useful for fainter Syferts in our sample as it will leave a large number of AGN undetected. The typical 3σ depth of the MaNGA observation in $\rm [O\, III]$ surface brightness can reach 10−17$\rm erg\, s^{-1}cm^{-2}arcsec^{-2}$. For our AGN sample, the majority of AGN spaxels have surface brightnesses above 10−16$\rm erg\, s^{-1}cm^{-2}arcsec^{-2}$ which is thus adopted in this work as the threshold to define the sizes of the ENLRs (hereafter R16). If all spaxels are above this threshold, we extrapolated the fitted $\rm [O\, III]$ surface brightness profile to determine R16 (see Section 3.4 for more detail). It should be noted that the surface brightness can be affected by cosmological dimming, which has a scale factor of (1 + z)4 (Liu et al. 2013; Hainline et al. 2014). That is important for works trying to compare sample with different redshift, especially for high redshift quasars.

2021AandA...656A.122D__Triana_et_al._2015_Instance_1

Understanding how angular momentum and chemicals are transported in the interiors of stars (and planets) along their evolution is one of the key challenges of modern stellar (and planetary) astrophysics. Indeed, rotation modifies their structure, their chemical stratification, their internal flows and magnetism, and their mass losses and winds (e.g. Maeder 2009; Mathis et al. 2013; Aerts et al. 2019, and references therein). In this quest, asteroseismology has bought a fundamental breakthrough by demonstrating that all stars are the seat of a strong extraction of angular momentum during their evolution in comparison with the predictions by stellar models taking the rotation into account following the standard rotational transport and mixing theory (Eggenberger et al. 2012; Marques et al. 2013; Ceillier et al. 2013; Cantiello et al. 2014; Ouazzani et al. 2019). This was first obtained thanks to mixed pulsation modes splitted by rotation propagating in evolved low- and intermediate-mass stars (Beck et al. 2012, 2014, 2018; Mosser et al. 2012; Deheuvels et al. 2012, 2014, 2015; Deheuvels et al. 2020; Di Mauro et al. 2016; Triana et al. 2017; Gehan et al. 2018; Tayar et al. 2019). Then, observations of oscillation modes in F- and A-type stars (Kurtz et al. 2014; Saio et al. 2015; Bedding et al. 2015; Keen et al. 2015; Van Reeth et al. 2015, 2016, 2018; Schmid & Aerts 2016; Murphy et al. 2016; Sowicka et al. 2017; Guo et al. 2017; Saio et al. 2018, 2021; Mombarg et al. 2019; Li et al. 2019, 2020; Ouazzani et al. 2020) and in B-type stars (Pápics et al. 2015, 2017; Triana et al. 2015; Moravveji et al. 2016; Kallinger et al. 2017; Buysschaert et al. 2018; Szewczuk & Daszyńska-Daszkiewicz 2018; Pedersen et al. 2021; Szewczuk et al. 2021) provided us new Rosetta stones to constrain the transport of angular momentum in the whole Hertzsprung-Russell diagram. More particularly, this pushes gravity- and gravito-inertial mode pulsators such as γ-Doradus and SPB stars at the forefront of this research. For instance, recent theoretical developments have demonstrated how it is possible to probe stellar internal rotation in γ-Doradus stars from their surface to their convective core (Ouazzani et al. 2020; Saio et al. 2021). These stars are rapid rotators for a large proportion of them. Therefore, it is necessary to study gravito-inertial modes. These modes are gravity modes, which propagate only in stably stratified stellar radiation zones under the action of the restoring buoyancy force in the absence of rotation, which are modified by rotation. If their frequency is super-inertial (i.e. above the inertial frequency 2Ω, Ω being the stellar angular velocity), they are propagating in stellar radiation zones and evanescent in convective regions. If their frequency is sub-inertial (below 2Ω) they propagate in an equatorial belt in radiation zones and they become propagative inertial waves in convective regions (e.g. Dintrans & Rieutord 2000; Mathis et al. 2014). The challenge of studying these waves is that the equation describing their dynamics are intrinsically bi-dimensional and non-separable (Dintrans et al. 1999; Prat et al. 2016, 2018; Mirouh et al. 2016). This makes the development of seismic diagnosis difficult analytically (Prat et al. 2017) or expansive in computation time when using 2D oscillation and stellar structure codes (e.g. Ouazzani et al. 2017; Reese et al. 2021) in the general case.

2018AandA...613A..15S__Becker_et_al._2016_Instance_1

In this study, we have outlined and successfully tested a refined technique to measure in contemporary lensing surveys the scale-dependent galaxy bias down to non-linear scales of k ~ 10 h−1 Mpc for lens galaxies at z ≲ 0.6. To test our reconstruction technique, we employ a fiducial survey with a sky coverage of ~ 1000 deg2, and a photometry and a survey depth as in CFHTLenS. To construct realistic samples of lenses and sources, we have prepared mock catalogues that are consistent with those used in SES13 and Saghiha et al. (2017). Despite some variations in survey depth and area, these survey parameters are similar to the ongoing Kilo-Degree Survey (KiDS), Dark Energy Survey (DES), or the survey with the Hyper Suprime-Cam (Kuijken et al. 2015; Becker et al. 2016; Aihara et al. 2018). If the galaxy-bias normalisation is perfect, our technique applied to these data can achieve a statistical precision within the range of 5–10% (68% CL), if similar lens and source samples are targeted, and a slightly better accuracy of 3−7% (68% CL; see Table 3). For the high-z samples, the accuracy will be somewhat higher with 3−5%. On the other hand, it is clear from our overview Table 4 that the accuracy of the galaxy-bias normalisation is in fact limited, mainly by our knowledge of the intrinsic alignment of sources, cosmological parameters, and the galaxy redshift distributions. With a broad knowledge of |Aia|≲ 2 and the specifications for the normalisation errors in Table 4, we conclude that systematic errors would potentially degrade the overall accuracy to approximately 15% for b(k) and 10% for r(k). For fully controlled intrinsic alignment of sources, these errors could be reduced by 5%. An additional reduction by 3% may be possible by controlling the redshift distributions (their mean and variance) in the normalisation to 1% accuracy. For the fiducial cosmology, the knowledge of Ωm is of most importance while the normalisation of the ratio statistics is less affected by σ8.

2016ApJ...817..156W__Yan_et_al._2014a_Instance_1

Recently, one of the hot topics in solar physics is the understanding of solar filaments in the corona, including their distribution, formation, eruption, and stability (Yang et al. 2008; Kong et al. 2015; Su et al. 2015; Yan et al. 2015). Martin (1998) and Gaizauskas (2002) have shown that convergence and cancellation of flux play an important role in the formation of filament channels and filaments. Flux ropes and magnetic dips represent the magnetic structures of filaments, which were reported by many authors (van Ballegooijen & Martens 1989; Mackay et al. 1999; Litvinenko & Wheatland 2005; Aulanier et al. 2006; Canou & Amari 2010). Many reports on the eruption of filaments are concerned with torus instability or/and kink instability (Török & Kliem 2003; Kliem & Török 2006; Török et al. 2010; Yan et al. 2014a, 2014b), and magnetic flux emergence and cancellation are also known to play a key role in these eruptions (Magara & Longcope 2003; Archontis & Török 2008; Yan et al. 2011). The transverse component of photospheric magnetic fields near the PIL increase after a filament’s eruption or flares (Liu et al. 2012; Sun et al. 2012; Wang et al. 2013). Sun et al. (2012) indicated that the substantial electric current increases with the emergence of flux during the formation of the filament. A downward collapse of coronal current after the eruption of the filament was also reported by Liu et al. (2012). Nonlinear force-free field (NLFFF) model extrapolation is the most powerful tool to reconstruct the magnetic field above the photosphere from photospheric vector magnetograms (VMs) thus far (Sakurai 1981; Wheatland et al. 2000; Amari et al. 2006; Canou & Amari 2010; Jiang et al. 2014), since the chromospheric and coronal magnetic fields are hard to measure exactly. Even so, it is still a long way to completely understand filaments. Regardless of its formative or eruptive process, and the variation of parameters including the electric current, magnetic field and plasma motion in the evolution of filament are not yet really clear. Investigating the electric current associated with the filament is helpful for understanding the characteristic of solar filaments.

2021MNRAS.507..175S___2008_Instance_1

Momentum and kinetic energy can be directly transferred to the gas, suppressing inflows. The fast-moving jets can also shock heat the surrounding gas. Many models have invoked kinetic jets to suppress cooling flows and SFRs in massive haloes (e.g. Dubois et al. 2010; Gaspari et al. 2012a; Li & Bryan 2014a; Prasad et al. 2015; Yang & Reynolds 2016a). Many models in the literature also invoke the idea that AGN can effectively drive strong pressure-driven outflows and offset cooling if a large fraction of the accretion energy is thermalized (Begelman 2004; Springel, Di Matteo & Hernquist 2005; Di Matteo, Springel & Hernquist 2005; Hopkins et al. 2006a, b, 2007, 2008; Johansson, Naab & Burkert 2009; Hopkins & Elvis 2010; Ostriker et al. 2010; Faucher-Giguère & Quataert 2012; Dubois et al. 2013; Barai et al. 2014; Weinberger et al. 2017a; Pillepich et al. 2018; Richings & Faucher-Giguère 2018a, b; Torrey et al. 2020). Physically, as the jet propagates, part of the kinetic energy can thermalize through shocks. Some studies have argued that the heat from those weak shocks can suppress cooling flows and SFRs in massive haloes (Yang & Reynolds 2016b; Li, Ruszkowski & Bryan 2017; Martizzi et al. 2019). The magnetic fields carried by the jet at its launch might also help suppress cooling flows by providing additional pressure support (Soker & Sarazin 1990; Beck et al. 1996, 2012), although our studies find that they have limited effects on global star formation properties of sub-L* galaxies (Su et al. 2017).1 Finally, CRs arise generically from processes that occur in fast shocks, so they could come from shocked winds or outflows. But they are particularly associated with relativistic jets from AGN (where they can make up the bulk of the jet energy; Berezinsky, Gazizov & Grigorieva 2006; Ruszkowski, Yang & Reynolds 2017b) and hot, relativistic plasma-filled ‘bubbles’ or ‘cavities’ (perhaps inflated by jets in the first place) around AGN. Different authors have argued that they could help suppress cooling flows by providing additional pressure support to the gas, driving pressurized outflows in the galaxy or CGM, or via heating the CGM/ICM directly via collisional (hadronic & Coulomb) and streaming-instability losses (Guo & Oh 2008; Sharma, Parrish & Quataert 2010; Enßlin et al. 2011; Fujita & Ohira 2011; Fujita, Kimura & Ohira 2013; Pfrommer 2013; Wiener, Oh & Guo 2013; Jacob & Pfrommer 2017a, b; Pfrommer et al. 2017; Ruszkowski et al. 2017a, b; Jacob et al. 2018).

2022AandA...663A..70F___2017_Instance_1

Out of these sites (a) might provide the conditions for a very weak r-process and νp-process, whether only up to Sr, Y, Zr or up to (but not beyond) the A = 130 peak is still debated (Wanajo et al. 2018; Curtis et al. 2019; Fischer et al. 2020a; Ghosh et al. 2022). (b) is a class of supernovae whose existence is put into question after recent re-determinations of the electron capture rate of 20Ne (Kirsebom et al. 2019a,b), but is not firmly excluded, however, leading to a too strong decline in abundances as a function of A for realistic Ye-conditions (Wanajo et al. 2011). (c) could plausibly lead to magnetars, neutron stars with surface magnetic fields of the order 1014 G, which form in ∼1 out of 10 of core collapse supernovae (e.g., Beniamini et al. 2019). Dependent on the initial fields, varying weak (probably dominating) to strong r-process conditions can be obtained, the latter, however, only for precollapse fields beyond 1012 G (Winteler et al. 2012; Mösta et al. 2014, 2015, 2018; Halevi & Mösta 2018; Nishimura et al. 2015, 2017; Bugli et al. 2020; Reichert et al. 2021). Case (d) has been proposed for a while. Dependent on the nuclear equation of state for massive core-collapse events, the collapse of the proto-neutron star to a black hole can be avoided (in a narrow stellar mass range) due to a quark-hadron phase transition with the right properties. The ejecta would experience a weak r-process, but populating even the actinides, however, with negligible abundances (Fischer et al. 2020b). Case (e) has been extensively discussed in the context of long-duration gamma-ray bursts (Woosley 1993; MacFadyen & Woosley 1999; MacFadyen et al. 2001). They involve the collapse of massive stars that rotate rapidly enough so that an accretion torus can form outside of the last stable orbit of a forming black hole, and they go along with relativistic polar and nonrelativistic torus outflows. This scenario has been proposed by Cameron (2003) as an r-process site and recently been examined in more detail by Siegel et al. (2019) and Siegel (2019). The remaining site, (f), is related to compact binary mergers (see Thielemann et al. 2017; Rosswog et al. 2017; Cowan et al. 2021, for overviews).

2021ApJ...921...18K__Kushwaha_et_al._2018a_Instance_1

The most unique and characteristic observational feature of blazars’ highly variable broadband emission is the broad bimodal SED extending from the lowest accessible EM band, i.e., the radio, to the highest accessible, i.e., GeV-TeV γ-rays. The broadband SED of all blazars can be categorized into three different spectral subclasses: low-energy-peaked (LBL/LSP), intermediate-energy-peaked (IBL/ISP), and high-energy-peaked (HBL/HSP; Fossati et al. 1998; Abdo et al. 2010), based on the location of the low-energy hump. A remarkable property of each spectral subclass is the stability of the location of the two peaks despite huge variations in flux and often spectral shape. Only in a few rare instances has an appreciable shift in the location of the peaks been observed, e.g., the 1997 outburst of Mrk 501 (Pian et al. 1998; Ahnen et al. 2018) and the activity of OJ 287 from the end of 2015 to the middle of 2017 (Kushwaha et al. 2018a, 2018b). Even these two cases are remarkably different. In the case of Mrk 501, the locations of both the peaks shifted to higher energies. On the contrary, in OJ 287, a shift in the location of only the high-energy peak was observed during the 2015–2016 activity (Kushwaha et al. 2018a, 2019), while in 2016–2017 a new broadband emission component overwhelmed the overall emission, appearing as an overall shift in both the peaks as revealed in the detailed study by Kushwaha et al. (2018b). With the SED being the prime observable for exploration of the yet-debated high-energy emission mechanisms, such changes offer invaluable insights about the emission processes. For example, in Mrk 501 the shift in both peaks strongly implies the same particle distribution for the overall emission, while for OJ 287 the shift of only the high-energy peak can be reproduced by either inverse Compton scattering of the broad-line region photon field (Kushwaha et al. 2018a) or emission of hadronic origin (Oikonomou et al. 2019; Rodríguez-Ramírez et al. 2020).

2017MNRAS.472..772M__Cassata_et_al._2015_Instance_1

Fig. 7 also shows that there is little evidence for a relation between the Ly α luminosity and M1500 for Ly α-selected sources at z = 5.7 in our UV and Ly α luminosity range. As both M1500 and LLy α are, to first order, related to the SFR, we would have expected a correlation. To illustrate this, we show lines at constant Ly α escape fractions (based on the assumption that SFRUV = SFRH α, case B recombination with T = 10 000 K and ne = 100 cm−3 and no attenuation due to dust). This result resembles the well-known Ando et al. (2006) diagram, which reveals a deficiency of luminous LAEs with bright UV magnitudes between z ≈ 5 and 6. More recently, other surveys also revealed that the fraction of high-EW Ly α emitters increases towards fainter UV magnitudes (e.g. Schaerer, de Barros & Stark 2011; Stark, Ellis & Ouchi 2011; Cassata et al. 2015). The lack of a strong correlation between M1500 and LLy α might indicate that the SFRs are bursty (because emission-line luminosities trace SFR over a shorter time-scale than UV luminosity), or that the Ly α escape fraction is anti-correlated with M1500 (such that Ly α photons can more easily escape from galaxies that are fainter in the UV). A possible explanation for the latter scenario is that slightly more evolved galaxies (which are brighter in the UV) have a slightly higher dust content (e.g. Bouwens et al. 2012), affecting their Ly α luminosity more than the UV luminosity. It is interesting to note that several galaxies lie above the 100  per cent Ly α escape fraction line. This implies bursty or stochastic star formation (which is more likely in lower mass galaxies with faint UV luminosities, e.g. Mas-Ribas, Dijkstra & Forero-Romero 2016), alternative Ly α production mechanisms to star formation (such as cooling), a higher ionizing production efficiency (for example due to a top-heavy IMF or binary stars, e.g. Gotberg, de Mink & Groh 2017), or dust attenuating Ly α in a different way than the UV continuum (e.g. Neufeld 1991; Finkelstein et al. 2008; Gronke et al. 2016).

2022ApJ...934..126K__Lotekar_et_al._2016_Instance_1

We considered a homogeneous, collisionless two species plasma consisting of electrons and ions (H+ ions) in the simulation model. The ambient plasma parameters for ions and electrons are given in Table 2. The ions and electrons are considered to be fluid and their dynamic is incorporated into the simulation model using the following model equations viz., the continuity, momentum, and pressure equations of each species, and the Poisson equation (Kakad et al. 2014) given by 1 ∂nj∂t+∂(njvj)∂x=0, 2 ∂vj∂t+vj∂vj∂x+1mjnj∂Pj∂x−qjmjE=0, 3 ∂Pj∂t+vj∂Pj∂x+γjPj∂vj∂x=0, 4 ∂E∂x=∑jqjnj/ϵ0. Here, the electric field E = −∂ϕ/∂x and the variables n j , P j , and v j are the plasma density, thermal pressure, and velocity of species j, respectively. The subscripts j = e and j = i are, respectively, used for electrons and ions. m j and q j represent the mass and the charge of species j, respectively. For electrons q e = −e and ions q i = e., ϵ 0 is the electric permittivity. In Equation (3), electrons and ions are treated as adiabatic with the same adiabatic index γ e = γ i = 3. The above set of equations is solved numerically. The spatial derivatives in these equations are solved using the fourth order central finite difference method and time derivatives are integrated using the leap-frog method to achieve second order accuracy. The details of the development of the simulation model are given in Kakad et al. (2013). We used a compensated filter to eliminate the small wavelength modes linked with such numerical noise (Lotekar et al. 2016; Kakad et al. 2016b). These numerical schemes are highly stable, and in the past, several electrostatic solitary wave structures have been modeled using such fluid simulations in multispecies plasmas (Kakad et al. 2014, 2016a). Δx and Δt are respectively considered as the grid size in spatial and time domain, and their values are taken in such a way that it fulfills the Courant–Friedrichs–Lewy condition, i.e., cΔtΔx≤1 , which is necessary for the convergence of the explicit finite difference method. Here, c is the speed of light. We performed two simulation runs in a one-dimensional system with the periodic boundary conditions by considering the observed ambient parameters as (i) T e = 60 eV, T i = 300 eV, n i0 = 15/cc, n e0 = 15/cc, V di = V de = −50 km s−1 (2) T e = 30 eV, T i = 300 eV, n i0 = 15/cc, n e0 = 15/cc, V di = V de = −50 km s−1 (see Table 2 for more details). In the simulations, we considered the real mass ratio, i.e., m i /m e = 1836. The background electron and ion densities satisfy the quasi-neutrality, i.e., n e0 = n i0 = n 0. For electron temperature, two values are considered based on the observations, i.e., 60 and 30 eV (see Figure 6(d)). The values of ω pi, ω pe, λ i , and λ e for the considered parameters are 5.1 × 103 rad s−1, 2.18 × 105 rad s−1, 33.3 m, and 14.9 m, respectively. To initiate the simulations, we used a localized Gaussian-type initial density perturbation in the equilibrium electron and ion densities given by 5 δn=Δnexp−x−xcl02. Here, Δn and l 0 are the amplitude and width of the initial density perturbations, respectively. Thus, the perturbed density at t = 0 is n j (x) = n j0 + δn. We consider the simulation system length as L x , and x c = L x /2 is the center of the simulation system. We performed two simulation runs for the parameters given in Table 2. For these simulation runs, we consider the time interval dt = 1 × 10−3, the grid spacing dx = 0.2, system length L x = 7000, l 0 = 1, and Δn = 0.1. Here, time is expressed in units of ω pi −1, space is in λ di, and density is in units of n i0.

2017AandA...607A..71G__Hansen_&_Oh_(2006)_Instance_2

An implication of the respective escape fractions of the two regimes is visible in Fig. 12. Here we show several values of NHI,cl for the static setup using τd,cl = 10-4 (empty symbols) and τd,cl = 1 (filled symbols), which correspond to metallicities of \hbox{$Z/Z_\odot = 0.63\left(\tau_{\rm d}/10^{-4}\right)\left(10^{17}\cm^{-2}/N_{\HI,\cl}\right)$}Z/Z⊙=0.63τd(/10-4)(1017 cm-2/NHI,cl) (Pei 1992; Laursen et al. 2009); this reaches clearly unrealistic values. However, as in this paper we are interested in the fundamental impact of the individual parameters, we also study these extreme values. Also shown in Fig. 12 (with a black [gray] solid line for the low [high] dust content) is the proposed analytic solution for fesc by Hansen & Oh (2006)(18)\begin{equation} f_{\rm esc}^{\rm HO06} = 1/{\rm cosh}(\!\sqrt{2 N_{\cl}\epsilon}), \label{eq:fescHO06} \end{equation}fescHO06=1/cosh(2Nclϵ),where for Ncl we used Eq. (8)(with (a1,b1) = (3/2, 2) as found in Sect. 4.1) and for the clump albedo (i.e., the fraction of incoming photons that are reflected) ϵ, we adopted a value of c1(1−e− τd,cl) with c1 = 1.6 [c1 = 0.06] to match the NHI,cl = 1022 cm-2 data points for τd,cl = 10-4 [τd,cl = 1]. The behavior for the low- and high-dust contents is quite different. On the one hand, the escape fractions versus NHI,cl scales for τd,cl = 1 (filled symbols in Fig. 12) as predicted by Hansen & Oh (2006) in their “surface scatter” approximation, that is, a larger clump hydrogen column density “shields” the dust better from the Lyα photons and thus prevents their destruction more efficiently. On the other hand, however, this is not the case for the low-dust scenario presented in Fig. 12 (with unfilled symbols) where a larger value of NHI,cl implies a lower fesc. This is because here the dust optical depth through all the clumps (shown in the black dotted line in Fig. 12) is lower than the accumulated dust optical depth through the subsequent random-walk clump encounters (black solid line), i.e., \hbox{$\exp(-4/3 \fc \tau_{\rm d, cl}) \lesssim f_{\rm esc}^{\rm HO06}$}exp(−4/3fcτd,cl)≲fescHO06. Consequently, configurations in the “free-streaming” regime can possess enhanced Lyα escape fractions compared to the “random walk” regime (see Sect. 5.2 for a more detailed discussion). Still, both cases possess (much) larger escape fractions than a homogeneous slab, which is shown in Fig. 12 with a black dashed line. Here, we use the derived escape fraction by Neufeld (1990) with NHI = 4/3 × fc1022 cm-2 and τd = 4/3fcτd,cl, i.e., with equal column densities as in the NHI,cl = 1022 cm-2 case.

2016MNRAS.457.2433P__Nolan_et_al._2012_Instance_1

From the result of the χ2 minimization, we found that the minimized χ2 values agree with the expected values, i.e. the computed χ2 are typically in the range of ($\mathrm{d.o.f.}-\sqrt{2 \mathrm{d.o.f.}}$, $\mathrm{d.o.f.}+\sqrt{2 \mathrm{d.o.f.}}$), where d.o.f. is the number of degrees of freedom. This means that the fits describe the observed data rather well. The only exception is with χ2 ≈ 20, which occurs for nearby AGN, z 0.2, and for the highest energy band, E > 10 GeV. Note that there is a strong contribution of the source, Mrk 421, in the first redshift interval at high energies, E > 10 GeV for quiescent states. Mrk 421 is a very hard spectrum γ-ray source with a photon index of ≈1.77 and its semiminor and semimajor axes at 68 per cent confidence are of 0$_{.}^{\circ}$0067 as derived in Nolan et al. (2012). Semiminor and semimajor axes of many 2FGL sources are derived with one order of magnitude higher uncertainties than those for Mrk 421 in the 2FGL catalogue (Nolan et al. 2012). We noted that the discrepancy between the observation and model is particularly strong in the annular bin, r 0$_{.}^{\circ}$05, for the redshift interval z 0.2 and for the highest energy band, E > 10 GeV. If we exclude photons from Mrk 421, then the minimized χ2 value is 7.5 and is consistent with the expected one. In the limit of a large number of counts in each bin, the likelihood is given by $\mathcal {L}=\text{exp}(-\chi ^{2}/2)$, so that minimizing χ2 is equivalent to maximizing the likelihood, $\mathcal {L}$. We found that the inclusion of a pair halo component in the model does not improve the likelihood value sufficiently to establish the presence of this pair halo component in the data. Therefore, we derived the one-sided 95 per cent upper limit on the fraction of photons attributable to a pair halo component by fitting the normalization of this component, for which we increase its normalization until the maximum likelihood decreases by 2.71/2 in logarithm. The computed upper limits are between 2 and 6 per cent depending on energy band and redshift interval. These upper limits are stronger than those obtained before. Note that the model for a point-like source used in the likelihood analysis is considered to be precisely established, however, the number of photons recorded during flaring states is close to those numbers of photons recorded during quiescent states for each of these redshift intervals. The expression, such as equation (1), leads to more conservative upper limits on the fraction of photons attributable to a pair halo component, since it takes the error bars assigned to the model into account. If the point-like source model is considered well established, then the error bars shown in Table 3 would decrease by a factor of ≈1.5.

2016AandA...586A..89C__Lo_2005_Instance_1

The knowledge of physical properties, the structure, and the kinematics of the matter in the vicinity of supermassive black holes (SMBH) is essential to build detailed models of the clumpy outflow and to test the disc-wind scenario. While X-ray variability studies can provide accurate information on the atomic and ionized matter on scales of the BLR, the radio emission from luminous H2O masers (the so-called “megamasers”) constitutes a fundamental instrument to study the geometry and kinematics of the molecular gas at sub-parsec distance from SMBH. H2O masers may trace distinct regions in the AGN environment, from nearly edge-on accretion discs to nuclear outflows in the form of jets or winds (for recent reviews see Henkel et al. 2005; Lo 2005; Greenhill 2007; Tarchi 2012). Very Long Baseline Interferometry (VLBI) and single-dish monitoring studies of disc-masers allow us to map accretion discs and to determine the enclosed dynamical masses (e.g. Kuo et al. 2011). Jet-masers observations, instead, can provide estimates of the velocity and density of jet material (Peck et al. 2003). H2O maser emission have been also found to be associated with nuclear winds at 1 pc from the nuclear engine. In particular, water maser observations in Circinus (Greenhill et al. 2003) and NGC 3079 (Kondratko et al. 2005) seem to have resolved individual outflowing torus clouds. In fact, Greenhill et al. (2003) discovered that the H2O masers in Circinus trace both a Keplerian disc and a wide-angle outflow which appears to be collimated by the warps of the disc. In NGC 3079, VLBI observations of the maser emission revealed a clumpy thick disc. In addition to that, four maser features were found to be located at high latitude above the disc (at ~0.5 pc from the disc plane) and interpreted as part of a nuclear wind by Kondratko et al. (2005). Proper motion measurements and comparison of these outflow-masers with their disc counterpart provide the most promising method for probing the structure and kinematics of the torus molecular clouds (Nenkova et al. 2008).

2021MNRAS.501.2522J__Mukherjee_&_Paul_2004_Instance_1

GX 301-2 is an HMXB consisting of a highly magnetized (B ∼ 4 × 1012 G, or even larger Doroshenko et al. 2010) pulsar and a B-type hyper-giant star Wray 977 (Vidal 1973; Kaper et al. 1995; Staubert et al. 2019). According to modelling of high-resolution optical spectra, Wray 977 has a mass of 43 ± 10 $\, \mathrm{M}_{\odot }$, a radius of 62 R⊙, and looses mass through powerful stellar winds at a rate of $\sim \! 10^{-5}\, \mathrm{M}_\odot \, {\rm yr}^{-1}$ with terminal velocity of 300 $\rm km\, s^{-1}$ (Kaper, van der Meer & Najarro 2006). The system is highly eccentric (e ∼ 0.46), with an orbital period of ∼41.5 d, and exhibits strong variation of the X-ray flux with orbital phase (Koh et al. 1997; Doroshenko et al. 2010). In particular, periodic outbursts at the orbital phase ∼1.4 d before the periastron passage (Sato et al. 1986), and a fainter one near the apastron passage are observed (Pravdo et al. 1995). The broad-band X-ray spectrum is orbital phase-dependent and can be approximately described as a power law with a high-energy cutoff and a cyclotron resonant scattering feature around 40 keV (Kreykenbohm et al. 2004; Mukherjee & Paul 2004; La Barbera et al. 2005; Doroshenko et al. 2010; Suchy et al. 2012; Islam & Paul 2014; Fürst et al. 2018; Nabizadeh et al. 2019). During the periastron flares, the source exhibits strong variability with an amplitude of up to a factor of 25, reaching a few hundreds mCrab in the energy band of 2–10 keV (e.g. Rothschild & Soong 1987; Pravdo et al. 1995). The flares are accompanied by the variability of the equivalent hydrogen column density ($\rm \mathit{ N}_{\rm H}$) and of the fluorescent iron lines, which is believed to be associated with clumpiness of the stellar wind, launched from the donor star (Mukherjee & Paul 2004). We note the clumpiness in this paper refers to any inhomogeneities in the stellar wind/stream, which are higher density regions, regardless of its specific formation mechanisms. On the other hand, Fürst et al. (2011) reported a long XMM–Newton observation in GX 301-2 around its periastron, which also exhibits systematic variations of the flux and $\rm \mathit{ N}_{H}$ at a time-scale of a few kiloseconds. Several wind accretion models, consisting of stellar winds and a gas stream, were proposed to explain the observed flares (e.g. Haberl 1991; Leahy 1991; Leahy & Kostka 2008; Mönkkönen et al. 2020).

2019MNRAS.486.3741H__Susa_et_al._2015_Instance_1

As the initial state of star-forming clouds, a critical Bonnor–Ebert (B.E.) density profile (Ebert 1955; Bonnor 1956) is adopted for each model. Note that the B.E. density profile or B.E. sphere is usually used as the initial condition of star-forming clouds (e.g. Matsumoto & Tomisaka 2004; Banerjee & Pudritz 2006; Machida et al. 2006a; Machida, Inutsuka & Matsumoto 2006b). The B.E. density profile is determined by the central density nc, 0 and isothermal temperature Tcl. The initial central density is set to $n_{\rm c,0}=10^4\, {\rm cm}^{-3}$ for all models. The temperature Tcl of each cloud is determined as the result of a one-zone calculation (for details, see Susa et al. 2015, and Paper I), and the results Tcl are given in Table 1. The cloud radius rcl, which depends on the initial cloud temperature, is also given in Table 1. To promote cloud contraction, the density is set to 1.8 times to the critical B.E. density profile (Machida & Hosokawa 2013). The initial cloud mass for each model is also listed in Table 1. Although the initial clouds have different radii and masses with different metallicities, the ratio α0 of thermal to gravitational energy, which significantly affects the cloud collapse (e.g. Miyama, Hayashi & Narita 1984; Tsuribe & Inutsuka 1999a,b), is the same for all models (α0 = 0.47). In addition, the ratio of rotational to gravitational energy in the initial cloud is set to β0 = 1.84 × 10−2 for all models (Goodman et al. 1993; Caselli et al. 2002). The initial magnetic field strength in each cloud is defined to satisfy μ0 = 3 (Troland & Crutcher 2008; Crutcher et al. 2010; Ching et al. 2017). The parameter μ0 is the mass-to-flux ratio of the initial cloud normalized by the critical value and is defined as (1) \begin{eqnarray*} \mu _0 &=& \frac{\left(M/\Phi \right)}{\left(M/\Phi \right)_{\rm cri}}, \end{eqnarray*} where M and Φ are the mass and magnetic flux of the initial cloud, respectively, and (M/Φ)cri is the ratio of the critical values of these parameters, which is (M/Φ)cri ≡ (2πG1/2)−1 (Nakano & Nakamura 1978). The direction of the magnetic field vector is parallel to the rotation vector (z-axis) in the initial cloud, in which a uniform magnetic field and rigid rotation are imposed.

2019AandA...629A..54U__Marinucci_et_al._2015_Instance_4

NGC 2110. NGC 2110 is another nearby (z = 0.00779, Gallimore et al. 1999), X-ray bright Seyfert galaxy. Diniz et al. (2015) report a black hole mass of 2 . 7 − 2.1 + 3.5 × 10 8 M ⊙ $ 2.7^{+ 3.5}_{- 2.1} \times 10^{8}\,{{M}_{\odot}} $ , from the relation with the stellar velocity dispersion. From BeppoSAX data, Malaguti et al. (1999) found the X-ray spectrum to be affected by complex absorption. This has been later confirmed by Evans et al. (2007), who find the Chandra+XMM–Newton data to be well fitted with a neutral, three-zone, partial-covering absorber. Rivers et al. (2014) find the Suzaku data to be well fitted with a stable full-covering absorber plus a variable partial-covering absorber. A soft excess below 1.5 keV is also present (Evans et al. 2007), and possibly due to extended circumnuclear emission seen with Chandra (Evans et al. 2006). No Compton reflection hump has been detected with Suzaku (Rivers et al. 2014) or NuSTAR (Marinucci et al. 2015), despite the presence of a complex Fe Kα line. According to the multi-epoch analysis of Marinucci et al. (2015), the Fe Kα line is likely the sum of a constant component (from distant, Compton-thick material) and a variable one (from Compton-thin material). Concerning the high-energy cut-off, ambiguous results have been reported in literature (see Table 1). Ricci et al. (2017) report a value of 448 − 55 + 63 $ 448^{+63}_{-55} $ keV, while Lubiński et al. (2016) report a coronal temperature of 230 − 57 + 51 $ 230^{+51}_{-57} $ keV and an optical depth of 0 . 52 − 0.13 + 0.14 $ 0.52^{+ 0.14}_{- 0.13} $ . From 2008–2009 INTEGRAL data, Beckmann & Do Cao (2010) report a cut-off of ∼80 keV with a hard photon index, but these results are not confirmed by NuSTAR (Marinucci et al. 2015). Indeed, only lower limits to the high-energy cut-off have been found with NuSTAR (210 keV: Marinucci et al. 2015), Suzaku (250 keV: Rivers et al. 2014) and BeppoSAX (143 keV: Risaliti 2002). No hard X-ray spectral variability has been detected by Caballero-Garcia et al. (2012) and Soldi et al. (2014) from BAT data, despite the significant flux variability.

2018ApJ...864..158L__Pino_&_Lazarian_2005_Instance_1

Inspection of Equation (25) reveals that for pitch angles satisfying , so that curvature drift energization is more efficient than generalized betatron energy loss during incompressible contraction/merging of small-scale flux ropes. When μ2 1/3, betatron energy loss dominates curvature drift energization. That is why the net acceleration obtained from the two competing acceleration mechanisms depends sensitively on the anisotropy characteristics of the energetic particle pitch-angle distribution as discussed above. Consider the following three possibilities: (1) If energetic particles maintain a highly beamed pitch-angle distribution (which requires negligible pitch-angle scattering), curvature drift energy gain strongly dominates generalized betatron energy loss, and for all practical purposes we have a first-order Fermi acceleration mechanism as a consequence of incompressible contraction or merging of curved flux-rope magnetic fields (de Gouveia dal Pino & Lazarian 2005; Drake et al. 2006, 2010). (2) If the energetic particle distribution stays purely isotropic (extremely strong pitch-angle particle scattering), we can average the last terms in Equation (25) over all μ values to find , indicating that the probability for curvature drift energy gain equals the probability for betatron energy loss (Drake et al. 2010). This supports the conclusion made above that net acceleration requires and depends only on the anisotropic part of the distribution. (3) The energetic particle distribution maintains a particle distribution with a small pitch-angle anisotropy (efficient pitch-angle scattering consistent with the diffusion approximation). In this case particle energization by incompressible contraction or merging of curved flux-rope magnetic fields becomes a second-order Fermi acceleration process (Drake et al. 2013; Zank et al. 2014; le Roux et al. 2015a). The small anisotropy option is supported by self-consistent particle simulations of turbulent magnetic reconnection and island formation at stacked primary current sheets in the absence of a guide field (Schoeffler et al. 2011; Drake et al. 2013), because energetic particles are scattered by fluctuations generated by plasma instabilities such as the firehose and magnetic mirror instabilities, resulting in energetic charged particle distributions with small anisotropies. However, in the presence of a strong guide field, particle simulations suggest larger anisotropies owing to weaker instabilities (Dahlin et al. 2017; Li et al. 2018).

2021MNRAS.506..813D__Fabricius_et_al._2014_Instance_1

Traditionally, GCs have been considered as relatively simple spherical, non-rotating, and almost completely relaxed systems. However, observational results obtained in the past few years are demonstrating that they are much more complex than previously thought. In particular, the classical simplified approach of neglecting rotation in GCs has become untenable from the observational point of view. In fact, there is an increasing wealth of observational results suggesting that, when properly surveyed, the majority of GCs rotate at some level. As of today, more than $50{{\ \rm per\ cent}}$ of the sampled GCs show clear signatures of internal rotation (e.g. Anderson & King 2003; Bellazzini et al. 2012; Fabricius et al. 2014; Bianchini et al. 2018; Ferraro et al. 2018; Kamann et al. 2018a; Lanzoni et al. 2018a,b; Sollima, Baumgardt & Hilker 2019). Moreover, evidence of rotation has also been reported for intermediate-age clusters (Mackey et al. 2013; Kamann et al. 2018b), young massive clusters (Hénault-Brunet et al. 2012; Dalessandro et al. 2021), and nuclear star clusters (Nguyen et al. 2018; Neumayer, Seth & Böker 2020) indicating that internal rotation is a common ingredient across dense stellar systems of different sizes and ages. On the theoretical side, the presence of internal rotation has strong implications on our understanding of the formation and dynamics of GCs and affects, for example, their long-term evolution (Einsel & Spurzem 1999; Ernst et al. 2007; Breen, Varri & Heggie 2017) and their present-day morphology (e.g. Hong et al. 2013; van den Bergh 2008). Moreover, signatures of internal rotation could be crucial in revealing the formation mechanisms of the so-called multiple stellar populations (MPs) in GCs (Bekki 2010; Mastrobuono-Battisti & Perets 2013; Hénault-Brunet et al. 2015) differing in terms of their light-element (such as He, Na, O, C, N) abundances (see Bastian & Lardo 2018; Gratton et al. 2019 for recent reviews on the subject), and which are observed in almost all GCs now. Differences in the rotation amplitudes of MPs have been observed in two cases so far, namely M 13 and M 80 (Cordero et al. 2017; Kamann et al. 2020)1 and in both clusters the Na-rich population (also known as second population or generation – SP) is found to rotate with a larger amplitude than the first population FP (Na-poor).

2017MNRAS.469..521K__Redfield_2007_Instance_1

Molecular CO gas is observed in the sub-mm with both single-dish telescopes (JCMT, APEX) and interferometers such as ALMA, the SMA or NOEMA. For the brightest targets, ALMA's high-resolution and unprecedented sensitivity allow us to obtain CO maps for different lines and isotopes showing the location of CO belts and giving an estimate of their mass (see the CO gas disc around β Pic, Dent et al. 2014; Matrà et al. 2017). Atomic species are also detected around a few debris disc stars. In particular, Herschel was able to detect the O i and C ii fine structure lines in two and four systems, respectively (e.g. Riviere-Marichalar et al. 2012, 2014; Roberge et al. 2013; Cataldi et al. 2014; Brandeker et al. 2016). Also, metals have been detected, using UV/optical absorption lines, around β Pictoris (Na, Mg, Al, Si and others, Roberge et al. 2006), 49 Ceti (Ca ii, Montgomery & Welsh 2012) and HD 32297 (Na i, Redfield 2007). Some of these metals are on Keplerian orbits but should be blown out by the ambient radiation pressure (Olofsson, Liseau & Brandeker 2001). It is proposed that the overabundant ionized carbon observed around β Pic, which is not pushed by radiation pressure could brake other ionized species due to Coulomb collisions with them (Fernández, Brandeker & Wu 2006). A stable disc of hydrogen has not yet been observed in these systems (Freudling et al. 1995; Lecavelier des Etangs et al. 2001) but some high velocity H i component (presumably falling on to the star) was detected recently with the HST/COS around β Pic (Wilson et al. 2017). All these observations need to be understood within the framework of a self-consistent model. Models of the emission of the gas around main sequence stars have been developed, but gas radial profiles were not derived self-consistently and often assumed to be Gaussian (e.g. Zagorovsky, Brandeker & Wu 2010) or not to be depleted in hydrogen compared to solar (as expected in debris discs, e.g. Gorti & Hollenbach 2004) or both (e.g. Kamp & Bertoldi 2000).

2018MNRAS.475.1104B__Leonard_et_al._2001_Instance_1

Observational evidence suggests that SNe IIn are aspherical and may have high polarization signals. An ∼ 20 per cent level of SN asphericity may result in a detectable 1 per cent linear polarization signal (Höflich 1991; Leonard & Filippenko 2005). While a number of efforts have been made to explain core-collapse SNe in terms of axisymmetric jets (Khokhlov et al. 1999; Wheeler, Meier & Wilson 2002; Wang et al. 2002), observational evidence in the form of loops in the Q/U plane suggests that even these axisymmetric models may not be sufficient for all types of core-collapse SNe (Hoffman et al. 2008; Wang & Wheeler 2008; Maund et al. 2009). In contrast to SNe IIn, SNe II-P generally have shown very low levels of polarization at early times (Leonard & Filippenko 2001; Leonard et al. 2002a; however, see Leonard et al. 2016; Mauerhan et al. 2017). The initially low polarization levels often rise during the plateau phase (e.g. Leonard et al. 2016), with a polarization angle that typically remains nearly fixed throughout (e.g. Leonard et al. 2001; Mauerhan et al. 2017). Occasionally, a sharp rise in the polarization signal is seen during the transition to the nebular phase (Leonard et al. 2006; Chornock et al. 2010), perhaps suggesting that the core of the SN is more aspherical than the early-time photosphere (Chugai et al. 2005; Chugai 2006). However, as demonstrated by the modelling of Dessart & Hillier (2011), it is also possible that even large asymmetries during the plateau phase will produce very little polarization, owing to the high optical depth to electron scattering and the fact that geometric information is lost due to multiple scatters. The ‘spike’ that is sometimes seen during the drop off of the plateau may, therefore, be more of an optical-depth effect (i.e. the ‘spike’ occurs when $\tau _{e^-}$ has decreased to unity) than a demonstration of increasing asphericity with depth in the atmosphere (Leonard et al. 2012). The picture for SNe IIn, on the other hand, is not as well understood. The primary reason for this is that an effective model for a SN IIn must not only account for the geometry of the SN ejecta, but also the geometry of the CSM interaction region (Chugai 2001). In such cases, the temporal evolution that multi-epoch spectropolarimetry provides becomes particularly important in establishing a physical model.

2020MNRAS.499.5230F__Tripp,_Savage_&_Jenkins_2000_Instance_1

Different methods have been proposed to detect the hot, highly ionized WHIM gas: detection in galaxy groups with Sunyaev–Zeldovich effect (Hill et al. 2016; de Graaff et al. 2019; Lim et al. 2020; Tanimura et al. 2020) using autocorrelation function measurements (Galeazzi et al. 2010), with absorption lines in quasar sightline (Kovács et al. 2019) and using CMB as a backlight (Ho, Dedeo & Spergel 2009). Given the challenges of X-ray data, observations at longer wavelengths (UV and optical) benefit from higher instrumental throughout, enhanced spectral resolution. By reverting to ground-based facilities, longer exposure times and larger number of targets become possible. Nevertheless, the UV lines have so far mostly been used to detect absorbing gas with temperature range $10^5\, \mathrm{K} \lt T \lt 10^6\, \mathrm{K}$ from either O vi (Tripp, Savage & Jenkins 2000; Danforth & Shull 2005; Danforth & Shull 2008; Tripp et al. 2008; Savage et al. 2014; Werk et al. 2014; Danforth et al. 2016; Sanchez, Morisset & Delgado-Inglada et al. 2016) or BLAs (Lehner et al. 2007; Danforth, Stocke & Shull 2010). Recently, Zastrocky et al. (2018) have constrained the Milky Way’s hot (T = 2 × 106 K) coronal gas using the forbidden 5302 Å transition of Fe xiv. Only recently, some highly ionized iron UV lines detected in emission have been used as diagnostics of gas at temperatures of T = 107 K. Out of several forbidden lines in the UV that could trace this gas temperature range, and from various species of highly ionized iron, the emission of [Fe xxi] is the brightest (Anderson & Sunyaev 2016). Anderson & Sunyaev (2018) report the discovery of [Fe xxi] in emission in a filament projected 1.9 kpc from the nucleus of M87. Theoretically, the highly ionized iron UV lines can be observed in absorption as well. The forbidden line of [Fe xxi], in particular, has the largest effective cross-section for absorption and a rest wavelength λ1354 Å, conveniently close to Ly α λ1215 Å.

2017AandA...602A..29B__Shepherd_1997_Instance_1

The MOJAVE survey provides access to excellent Very Long Baseline Array (VLBA) data taken at 15 GHz. This is of great value for investigating AGN properties on a statistical basis (e.g., Lister et al. 2016; Homan et al. 2015). Whereas the MOJAVE team is providing a statistical analysis of the complete sample, our approach is to select and focus on specific sources which appear to be special due to unique or rare properties. Although a statistical analysis of large numbers of sources is certainly of utmost importance and great value, a detailed analysis concentrating on peculiar properties – that are not necessarily common to the AGN population – can produce complementary results. We re-modeled fifty VLBA observations of 1308+326 obtained at 15 GHz (taken from the online MOJAVE archive webpage) between 1995.05 and 2014.07 with Gaussian components within the difmap-modelfit program (Shepherd 1997). The modelfit program fits image-plane model components to the visibilities in the uv plane. We did not apply self-calibration but used the calibration as provided by the MOJAVE team. Only circular components were used. The use of exclusively circular Gaussian components proved to be the most efficient way to parameterize the component properties. It reduces the number of free parameters, compared to the use of elliptical Gaussians, and allows a more secure identification of components across the epochs. Although it might have advantages to model the data of some of the epochs with elliptical components, our experience is that circular component modeling allows a more homogeneous analysis of all the epochs and more trustworthy identification of individual components in their long-term motion and evolution. Every epoch was modeled independently starting from a point source model. The errors were estimated from deviations in all parameters derived by calculating fits to models with ±1 component. All the images with model-fits superimposed are displayed in Figs. A.1–A.13. The parameters and corresponding uncertainties of the model-fits are listed in Table A.1. Components labeled with an “x” denote the presence of features that could not be reliably traced across the epochs. In addition we analyzed single-dish total intensity and polarization radio monitoring data at three radio frequencies, which was observed by the UMRAO monitoring program.

2016AandA...586A.156K__Osorio_et_al._(2011)_Instance_1

In this study, we use a model atom of Li i which was originally developed and tested by Cayrel et al. (2007) and Sbordone et al. (2010). For the purposes of the current work, the model atom was updated and now consists of 26 levels and 123 (96 of which are radiative) bound-bound transitions of Li i and the ground level of Li ii, with each level of Li i coupled to the continuum via bound-free transitions. (The ground state of Li ii in the current model atom is always in LTE, since lithium is mostly fully ionized throughout the model atmospheres studied in this work.) This renders the model atom complete up to the principal quantum number n = 6 and spectroscopic term \hbox{$^2{\rm F}^{\rm o}$}Fo2, with additional energy levels up to n = 9 and term 2D (Fig. 1). Data concerning atomic energy levels and transitions (level energies and statistical weights; wavelengths and Einstein coefficients of the bound-bound transitions) were taken from the NIST database. We used electron collisional excitation and ionization rates from the quantum mechanical computations of Osorio et al. (2011) for the energy levels of up to 5s (2S). Elsewhere, collisional excitation by electrons for radiatively permitted transitions was accounted for by using the classical formula of van Regemorter (1962), while the formula of Seaton (1962) was used to compute collisional electron ionization rates. To account for the collisional excitation by hydrogen, we used collisional excitation rates computed by Barklem et al. (2003), while the classical formula of Drawin (in the formulation of Lambert 1993) was used for radiatively permitted transitions when no quantum mechanical data were available. Hydrogen H–Li charge transfer rates were taken from Barklem et al. (2003) for the atomic levels up to 4p inclusive. Bound-free transitions resulting from collisions with hydrogen were expected to be inefficient and thus were ignored. Photoionization cross sections were taken from TOPBASE (Cunto et al. 1993). No scaling of collisional rates was applied in the calculations of bound-free and bound-bound transitions. Information about the energy levels and bound-bound radiative transitions, included in the present version of the Li i model atom, are provided in Tables A.1 and A.2, respectively. Twenty-seven transitions in the model atom are purely collisional. Collisional radiatively-forbidden transitions involving Li i levels beyond 5s were not accounted for since reliable quantum-mechanical data for these transitions are not available. We note that the role of the omitted transitions between the higher levels is minor: when they are taken into account using the formula of Allen (1973), collision strength Ω = 1, the change in the estimated abundance (which directly applies to abundance corrections, too) is always less than 0.05 dex, with typical values being significantly smaller.

2016MNRAS.462.2275K__Porubcan,_Kornos_&_Williams_2004_Instance_1

Currently, the concept of the existence of genetic relations between comets and meteoroid streams as well as near-Earth asteroids (NEAs) is generally accepted. As a consequence, a new definition, the ‘comet–asteroid–meteoroid complex’, was introduced for the indication of the families of objects with a common origin. The certainty of the existence of such associations has been proved by numerous studies of the dynamical properties of some small bodies. For instance, the association of comet 2P/Encke with the Taurid meteoroid stream was confirmed by a number of investigations and does not raise any doubts. Furthermore, it was shown that more than 40 asteroids belong to the Taurid complex, i.e. they move on orbits close to those of the comet Encke and the Taurid stream. A cometary nature of these NEAs was suggested and a conclusion was made that they are either extinct fragments of comet Encke or represent (together with comet Encke) the remnants of a larger comet-progenitor of the stream (Asher, Clube & Steel 1993a; Babadzhanov 2001; Porubcan, Kornos & Williams 2004, 2006; Babadzhanov, Williams & Kokhirova 2008a; Rudawska, Vaubaillon & Jenniskens 2012a,b; Madiedo et al. 2013). This family of near-Earth objects with a common origin is named the Taurid comet–asteroid–meteoroid complex. The Quadrantid comet–asteroid–meteoroid complex is another well-known set of related NEOs that very probably includes comet 96P/Machholz 1 and certainly NEA 2003EH1 and the Quadrantid meteoroid stream, producing eight meteor showers observable on the Earth. It was shown that asteroid 2003EH1 is in fact the extinct fragment of a parent comet of the Quadrantid stream (Jenniskens 2004; Williams et al. 2004; Babadzhanov, Williams & Kokhirova 2008b; Neslusan, Hajdukova & Jakubik 2013). It turned out that the Piscid meteoroid stream contains three related NEAs moving within the stream and representing the extinct debris of a larger comet-progenitor of this complex (Babadzhanov & Williams 2007; Babadzhanov, Williams & Kokhirova 2008c). Based on investigation of the dynamical properties, it was found that three NEAs with similar comet-like orbits are associated with the ι Aquariid meteoroid stream and this asteroid–meteoroid complex is the result of the break-up of a parent comet (Babadzhanov, Williams & Kokhirova 2009).

2016ApJ...822...22O__Orlando_et_al._2015_Instance_1

We used the FLASH code (Fryxell et al. 2000) to perform the calculations. In particular we solved the equations for compressible gas dynamics with the FLASH implementation of the piecewise-parabolic method (Colella & Woodward 1984). The radiative losses Λ in Equation (2) are calculated through a table lookup/interpolation method. Also we extended the code by additional computational modules to calculate the deviations from electron-proton temperature-equilibration and the deviations from equilibrium of ionization of the most abundant ions. For the former, we calculated the ion and electron temperatures in each cell of the post-shock medium, taking into account the effects of Coulomb collisions (see Orlando et al. 2015 for the details of the implementation). According to Ghavamian et al. (2007), first the electrons are assumed to be heated at the shock front almost istantaneously up to kT ∼ 0.3 keV by lower hybrid waves. This istantaneous heating does not depend on the shock Mach number and is expected for fast shocks (i.e., >103 km s−1) as those simulated here. Then we considered the effects of the Coulomb collisions to calculate the evolution of ion and electron temperatures in each cell of the post-shock medium in the time Δtj = t − tshj, where tshj is the time when the plasma in the jth domain cell was shocked and t is the current time. The time tshj is stored in an additional passive tracer added to the model equations. To estimate the deviations from equilibrium of ionization of the most abundant ions, we adopted the approach suggested by Dwarkadas et al. (2010). In fact, this approach ensures high efficiency in the calculation (expecially in the case of 3D simulations as in our case) as well as a reasonable accuracy in the evaluation of the non-equilibrium of ionization effects. The approach consists of the computation of the maximum ionization age in each cell of the spatial domain τj = nejΔtj, where nej is the electron density in the jth cell and Δtj is the time since when the plasma in the cell was shocked (see above).

2022AandA...660A.135V__Spina_et_al._2016_Instance_1

The first study, to our knowledge, to notice the net increase in the abundance of slow (s) neutron capture elements in young stellar populations is D’Orazi et al. (2009), in which the abundance of barium in young star clusters was seen to be higher than in the older ones. Maiorca et al. (2011, 2012) added a few more elements with important s-process contributions (yttrium, zirconium, lanthanum, and cerium), confirming the increasing trend towards younger ages. Subsequently, a number of works have attempted to both clarify the origin of this increase (see, e.g., Bisterzo et al. 2014; Mishenina et al. 2015; Trippella et al. 2016; Magrini et al. 2018; Spina et al. 2018; Busso et al. 2021) and to use their abundances to estimate the ages of stars, often using neutron capture s-process elements in combination with other elements with opposite behaviours, such as α elements – that we indicate as chemical clocks – and thus maximising the dependence of the relationship with age (see, e.g., Tucci Maia et al. 2016; Nissen 2016; Feltzing et al. 2017; Fuhrmann et al. 2017; Slumstrup et al. 2017; Titarenko et al. 2019). Once the existence of a relationship between age and chemical clocks was established (see, e.g., Spina et al. 2016; Delgado Mena et al. 2019; Jofré et al. 2020), the next steps were the following: (i) to clarify the applicability of these relationships with luminosity class (dwarf or giant) (see, e.g., Tucci Maia et al. 2016; Slumstrup et al. 2017; Casamiquela et al. 2021), metallicity (see, e.g., Feltzing et al. 2017; Delgado Mena et al. 2019; Casali et al. 2020), and population type (thin disc, thick disc, halo) (see, e.g., Titarenko et al. 2019; Nissen et al. 2020; Tautvaišienė et al. 2021), or even in dwarf galaxies (Skúladóttir et al. 2019; ii) to calibrate them with a sample of stars with reliable age determination, which are usually open star clusters (OCs), solar twins, or targets with asteroseismic observations. Finally, it is essential to understand whether these relationships are valid throughout the Galactic disc, or whether they are necessarily position-dependent. For the first time, Casali et al. (2020) applied the relations derived from a large sample of solar-like stars located in the solar neighbourhood and noted that they fail to reproduce the ages of star clusters in the inner disc. They concluded that the relationship between age and chemical clocks is not universal and that it varies with galactocentric position. Later, Magrini et al. (2021b) suggested that the differences in the relationships between age and chemical clocks in different parts of the Galactic disc are due to the strong dependence on the metallicity of the yields of low-mass stars, which produce s-process elements during the final stages of their evolution. Casamiquela et al. (2021) used red clump stars in open clusters to investigate the age dependence of several abundance ratios, including those that contain s-process and α elements. They found that the relationship between [Y/Mg] and ages outlined by open clusters is similar to the one found using solar twins in the solar neighbourhood. They also found that the abundance ratios involving Ba are those with the highest correlation with age. However, they also note that as one moves away from the solar neighbourhood, the dispersion increases and is in agreement with the findings of Casali et al. (2020), which attributed this to the spatial variation of the star formation history along the galactocentric radius.

2021MNRAS.505.5833F__Lesgourgues_2011_Instance_1

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

2019ApJ...875...63M__Stern_et_al._2019_Instance_1

The technique of weak lensing offers direct measurement of the total matter distribution of a galaxy cluster (baryonic and dark matter), and can thus provide an unbiased mass calibration. Weak lensing manifests itself as small but coherent distortions of distant galaxies that result from the gravitational deflection of light due to foreground structures (e.g., Kaiser 1992). Cluster weak lensing appears as a tangential shear of background galaxy shapes around a cluster. Numerous attempts to calibrate SZ masses have been made in the literature using ACT clusters (Miyatake et al. 2013; Jee et al. 2014; Battaglia et al. 2016), SPT clusters (McInnes et al. 2009; High et al. 2012; Schrabback et al. 2018; Stern et al. 2019; Dietrich et al. 2019), Planck clusters (von der Linden et al. 2014b; Hoekstra et al. 2015; Penna-Lima et al. 2017; Sereno et al. 2017; Medezinski et al. 2018a), Planck and SPT clusters (Gruen et al. 2014), and other massive cluster samples (Marrone et al. 2009, 2012; Hoekstra et al. 2012; Smith et al. 2016). The mass calibration is often parameterized as 1 where MSZ is the SZ mass and Mtrue is the true cluster mass, which for this paper we take to be the weak-lensing mass MWL. This ratio can be taken for individual clusters or for an ensemble average and these values will be consistent as long as the appropriate weights are used (Medezinski et al. 2018a). Recently, Planck Collaboration et al. (2016d) reported a disagreement between 1−b obtained by weak-lensing calibrations of Planck SZ cluster masses (e.g., von der Linden et al. 2014b; Hoekstra et al. 2015) and that inferred from reconciling the Planck primary CMB parameters with the Planck SZ cluster counts. This disagreement is not statistically significant (∼2σ) and will decrease after accounting for additional bias corrections, like Eddington bias (Battaglia et al. 2016) and new optical depth measurements (Planck Collaboration et al. 2016e). However, if such a disagreement persists as the precision of cluster measurements improves, then it could reveal the need for extensions to the standard cosmological model (Planck Collaboration et al. 2016c), like a non-minimal sum of neutrino masses (e.g., Wang et al. 2005; Shimon et al. 2011; Carbone et al. 2012; Mak & Pierpaoli 2013; Louis & Alonso 2017; Madhavacheril et al. 2017), or illuminate additional systematic effects in cluster abundance measurements.

2019AandA...625A.148D__Li_et_al._2011_Instance_1

With a stellar mass of 5 × 1010 M⊙ (Viaene et al. 2014), Andromeda belongs to the transition regime between the active blue-sequence galaxies and passive red-sequence galaxies (e.g. Bower et al. 2017; Baldry et al. 2006), which happens around the stellar mass of 3 × 1010 M⊙ (e.g. Kauffmann et al. 2003). This galaxy is a prototype galaxy from the Local Group where the star formation has been quenched in the central part. Andromeda hosts both very little gas and very little star formation, while the black hole is basically quiet and has some murmurs (Li et al. 2011). In a previous study about M 31 nucleus, Melchior & Combes (2017) have shown that there is no gas within the sphere of influence of the black hole. Indeed, the gas has been exhausted. Most scenarios of the past of evolution of Andromeda reproduce the large scale distribution and show evidence of a past activity rich in collisions (Ibata et al. 2001, 2014; Thilker et al. 2004; Gordon et al. 2006; McConnachie et al. 2009; Miki et al. 2016; Hammer et al. 2018). However, the exact mechanism quenching the activity in the central kiloparsec is still unknown (Tenjes et al. 2017). Block et al. (2006) proposed a frontal collision with M 32, which could account for the two ring structures observed in the dust distribution. Melchior & Combes (2011, 2016) showed the presence of gas along the minor axis and support the scenario of the superimposition of an inner 1 kpc ring with an inner disc. Melchior & Combes (2013) estimated a minimum total mass of 4.2 × 104 M⊙ of molecular gas within a (projected) distance to the black hole of 100 pc. This is several orders of magnitude smaller than the molecular gas present in the central molecular zone of the Milky Way (Pierce-Price et al. 2000; Molinari et al. 2011). In the Galaxy, while large amounts of dense gas are present in the central region, Kruijssen et al. (2014) discussed the different processes that combine to inhibit star formation, which was observed to be a factor ten times weaker than expected (e.g. Leroy et al. 2008).

2021MNRAS.502..915C__Cisneros-Parra_1970_Instance_1

Under the Applegate model, the change in orbital period is directly related to the change in the companion star’s gravitational quadrupole moment Q (Applegate & Patterson 1987), (8)$$\begin{eqnarray*} \frac{\Delta P_{\rm orb}}{P_{\rm orb}} = -9\frac{\Delta Q}{M_{\rm c} A^2}, \end{eqnarray*}$$where A = x(1 + q)/sin i is the orbital separation. For comparison, the total quadrupole moment induced by the spin of the companion star and the tidal distortion in the pulsar’s gravitational field is (Voisin, Breton & Summers 2020a) (9)$$\begin{eqnarray*} \frac{Q}{M_{\rm c} A^2} = -\frac{2}{9} k_2 \left(\frac{R_{\rm c}}{A}\right)^5 \left(4 q + 1\right), \end{eqnarray*}$$where Rc is the radius of the companion star and k2 is the apsidal motion constant, a parameter describing the deformability of the companion star (Sterne 1939). For solar-type stars k2 ∼ 0.035 (Ogilvie 2014), while if we assume that redback companions are akin to the companions in CV systems whose outer envelopes have also been stripped through accretion, then we may expect a smaller value k2 ∼ 10−3 (Cisneros-Parra 1970). For J2039, the hyperparameter $h = 3.9^{+2.2}_{-1.2}$ s corresponds to the typical fractional amplitude for the variations in orbital phase. Taking the simpler squared exponential covariance function of equation (4) corresponding to n → ∞, then the deviations in orbital period have covariance function, (10)$$\begin{eqnarray*} K_{\Delta P_{\rm orb}/P_{\rm orb}}(t_1,t_2) &=& \frac{\partial ^2 K}{\partial t_1 \partial t_2} \nonumber\\ &=& \frac{h^2}{l^2} \exp \left(\!-\frac{(t_1 - t_2)^2}{2\ell ^2}\!\right) \left(\!1 - \frac{(t_1 - t_2)^2}{\ell ^4}\!\right). \end{eqnarray*}$$The typical (fractional) amplitude of the orbital period variations is therefore ΔPorb/Porb ∼ h/ℓ = (3 ± 1) × 10−7, corresponding to $\Delta Q / Q \sim 3\times 10^{-5} k_2^{-1}$. The time-varying component to the gravitational quadrupole moment is therefore required to be of order a few per cent of the total expected quadrupole moment at most to explain the observed orbital period variations. From this, it seems plausible that the observed period variations can be powered by quadrupole moment changes, without requiring that a large fraction of the star be involved in the process. The required fractional quadrupole moment changes are very similar to those recently calculated for the companion to the black widow PSR J2051–0827 by Voisin et al. (2020b), despite the large difference in their masses.

2018MNRAS.478...69A__Borucki_2016_Instance_1

The complexity of non-adiabatic pulsations and their coupling to the convection has posed many problems since the field’s inception and still does. The main problem lies in our, so far, limited understanding of the interaction between convection and pulsations. However, several important steps forward have already been taken, and several recent reviews on the topic exist (see for example Houdek & Dupret 2015; Samadi, Belkacem & Sonoi 2015). The case of solar pulsational stability has been studied in detail both theoretically (Balmforth 1992) and observationally (Chaplin et al. 1997; Komm, Howe & Hill 2000), while the space missions CoRoT (Baglin et al. 2006) and Kepler (Borucki et al. 2010; Borucki 2016) have provided high-quality seismic data for stars of different flavours against which we can test models and further our understanding of stellar pulsations. Appourchaux et al. (2014) analysed oscillation mode linewidths for a number of Kepler main-sequence solar-like stars and found interesting relationships between linewidths, frequencies, and effective temperatures. Using CoRoT observations Samadi et al. (2012) showed that non-adiabatic effects are present and non-negligible in red giant stars. Houdek & Gough (2002) modelled damping rates and velocity amplitudes of the red giant ξ Hydrae, while Dupret et al. (2009) computed theoretical amplitudes, lifetimes, and heights in the frequency power spectrum of oscillation modes at different stages of red giant evolution, including the phase of core helium burning, and Grosjean et al. (2014) computed synthetic power spectra for mixed modes in red giants. Belkacem et al. (2012) were able to reproduce observed Γ versus Teff across the HR-diagram including both main-sequence and red giant stars. However, calculations of frequency-dependent damping rates for red giant stars have so far not been able to survive comparisons with observations. Handberg et al. (2017), hereafter referred to as H17, obtained precise frequencies and linewidths for a sample of red giants in NGC 6819 by means of extensive, careful peakbagging. Here, we compute frequency-dependent damping rates for a selection of red giant branch (RGB) stars in the H17 sample. This is done via a non-adiabatic stability calculation (Houdek et al. 1999) for which we obtain the convective fluxes from a non-local, time-dependent convection model (Gough 1977a,b) partly calibrated through 3D convection simulations (Trampedach et al. 2013).

2020AandA...635A.121M__Stolker_et_al._2016_Instance_1

As scattered light imaging is sensitive to the stellar irradiation, it allows one to search for misalignments between various disk regions. While studying the morphology of the innermost disk region is challenging due to its very small radial extent, often marginally resolvable by optical interferometry (Lazareff et al. 2017), scattered light imaging of the outer disk can indirectly reveal the presence of a misaligned inner disk. In this scenario, depending on the misalignment angle, the outer disk image will show narrow shadow lanes (e.g., Pinilla et al. 2015; Stolker et al. 2016; Benisty et al. 2017; Casassus et al. 2018), broad extended shadows (Benisty et al. 2018) or low-amplitude azimuthal variations (Debes et al. 2017; Poteet et al. 2018). In some cases, studies of the CO line kinematics support a misalignment between inner and outer disk regions (Loomis et al. 2017; Pérez et al. 2018). The exact origin of such a misalignment is still unclear. In the case of T Tauri stars, if the stellar magnetic field is inclined, it can warp the innermost edge of the disk, which would then rotate at the stellar period (AA Tau; Bouvier et al. 2007). Alternatively, inner and outer disk regions can have different orientations if the primordial envelope had a different angular momentum vector orientation at the time of the inner/outer disk formation (Bate 2018). Other scenarios involve the presence of a massive companion/planet that is inclined with respect to the disk. If the companion is massive enough, the disk can break into two separate inner and outer disk regions, that can then precess differently and result in a significant misalignment between each other (e.g., Nixon et al. 2012; Facchini et al. 2013; Nealon et al. 2018; Zhu 2019). A clear example of such a scenario is the disk around HD 142527, in which an M-star companion was detected (Biller et al. 2012), likely on an inclined and eccentric orbit (Lacour et al. 2016; Claudi et al. 2019). Dedicated hydrodynamical simulations successfully reproduce most of the observed features in this disk (eccentric cavity, spiral arms, misaligned inner disk and shadows; Price et al. 2018).

2020ApJ...897...73M__Dugair_et_al._2013_Instance_1

Many Be-binary systems were observed during their outbursts, which offered interesting results (Bildsten et al. 1997; Reig 2011). Bright X-ray outbursts are observed in Be binaries, most likely during the periastron passage of its neutron star through the circumstellar disk of its companion. Depending on the amount of matter released from its companion and the geometry of the binary system, rare as well as regular outbursts are observed during its binary orbit (Okazaki & Negueruela 2001; Okazaki et al. 2002; Okazaki 2016). The pulse characteristics of some of these, such as EXO 2030+375 (Parmar et al. 1989), Cepheus X-4 (Mukerjee et al. 2000), and XTE J1946+274 (Paul et al. 2001), were studied in detail during their outburst activities. Studies on pulse characteristics offer information on the pulsar geometry and the mechanism underlying its emitted pulse profile. The shape of the emitted pulse depends on modes of accretion inflows, source luminosity, geometry of accretion column, and the configuration of its magnetic field with respect to an observer’s line of sight (Parmar et al. 1989). Therefore, such studies offer understanding of pulsars in binary system and disk–magnetosphere interaction during the process of mass accretion, which affects its emitted radiation. Quasi-periodic oscillations (QPOs) have been detected from many Be binaries, such as A0535+262 (Finger et al. 1996; Finger 1998), EXO 2030+375 (Angelini et al. 1989), 4U 0115+63 (Soong & Swank 1989; Heindl et al. 1999; Dugair et al. 2013), and V0032+53 (Qu et al. 2005). Studies of QPOs offer rich information about the accretion torque onto the neutron star, thermodynamic properties of the inner accretion disk, and electrodynamics of the disk–magnetosphere interaction of the neutron star. Details on sources with observed QPOs, their frequencies, and other features along with pulsar spin frequencies, etc. have been given in tabular form by Devasia et al. (2011), Ghosh (1998), and Mukerjee et al. (2001). Some of these transient Be-binary pulsars such as A0535+26 (50 mHz, 9.7 mHz), 1A 1118–61 (92 mHz, 2.5 mHz), XTE J1858+034 (110 mHz, 4.53 mHz), EXO 2030+375 (200 mHz, 24 mHz), SWIFT J1626.6–5156 (1000 mHz, 65 mHz), XTE J0111.2–7317 (1270 mHz, 32 mHz), as well as a persistent Be binary, X Per (54 mHz, 1.2 mHz), and an OB-type binary, 4U 1907+09 (69 mHz, 2.27 mHz), showed higher QPO frequency compared to their respective spin frequency as mentioned in order inside parentheses (Devasia et al. 2011). These cover an interestingly wide range of QPO frequencies between 50 and 1270 mHz for these pulsars. Studies of cyclotron absorption features, if present in the spectrum, enable us to determine the strength of the surface magnetic field of the neutron star and offer insight into the line-producing region and the structure of the accretion column and its geometry (Staubert et al. 2019). Cyclotron absorption features thus have provided an important diagnostic probe for detailed studies of neutron star binaries since their discovery in the spectrum of Her X-1 (Truemper et al. 1978). There are several reports on the detection of cyclotron absorption features in the spectrum of many Be binaries, starting at a lower energy, from ∼10 keV (Jun et al. 2012; DeCesar et al. 2013), to a higher energy, at ∼100 keV (La Barbera et al. 2001). A detailed compilation of such sources and studies is given in Staubert et al. (2019) and Maitra (2016). It has been observed in detailed studies that some sources show a wide variation in their cyclotron line energy with respect to their pulse phase and source luminosity, and time, such as Vela X-1, Cen X-3, and Her X-1 (Staubert et al. 2019). These interesting properties help in understanding the nature of these sources and also offer insight into their underlying physical properties governing such changes.

2021ApJ...921...25C__Way_et_al._2017_Instance_1

Only a few GCM studies have previously considered isolated examples of higher-order spin–orbit resonance effects on climate (Wordsworth et al. 2010; Yang et al. 2013, 2020; Turbet et al. 2016; Boutle et al. 2017; Del Genio et al. 2019b). To our knowledge, no previous work has incorporated geothermal heating into a 3D GCM in the context of evaluating IHZ limits. Haqq-Misra & Kopparapu (2014) did report the impact of a 2 W m−2 surface heating in a highly idealized GCM for a synchronous rotation planet, while Haqq-Misra & Heller (2018) conducted idealized GCM simulations of tidally heated exomoons in synchronous rotation with the host planet. Yang et al. (2013) showed that at 2:1 and 6:1 resonances with a static/slab ocean (see Section 2.2.2 of Way et al. 2017), Bond albedo is lower than it is for synchronous rotation and decreases rather than increases with incident stellar flux, thus destabilizing the climate as the planet approaches the IHZ. Turbet et al. (2016) considered a 3:2 resonance state and static ocean for Proxima Centauri b, assuming zero eccentricity. Boutle et al. (2017) simulated the same planet in 3:2 resonance and 0.3 eccentricity; that study uses a thin static ocean surface, which produced a longitudinal double-eyeball pattern of surface liquid water roughly coincident with the maxima in stellar heating. Wang et al. (2014) found zonally symmetric temperatures for 3:2 and 5:2 resonances with a static ocean, but Dobrovolskis (2015) showed that this was the result of an incorrect spatial pattern of instellation. Del Genio et al. (2019b) performed the first dynamic ocean simulation of a planet in a higher-order resonance, showing that despite the double maximum in instellation at 3:2 resonance, the resulting climate has a tropical liquid waterbelt spanning the planet because of the ocean thermal inertia and heat transport. Yang et al. (2020) used a dynamic ocean and focused on the outer edge of the habitable zone by considering the effect of sea ice drift on snowball transitions for nine exoplanets, including a sampling of the 3:2 resonance, also with zero eccentricity.

2021MNRAS.503..354G__Cantat-Gaudin_et_al._2020_Instance_1

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

2018MNRAS.476.4510P__Hobbs,_Edwards_&_Manchester_2006_Instance_1

The modulation of an extra-solar signal can, if working in terms of signal phase, be expressed as a time modulation, e.g. for a phase evolution given by (1) \begin{eqnarray} \phi (t) = \phi _0 + 2\pi f_0\left( t - t_0 + \Delta \tau (t) \right), \end{eqnarray} where t is the time of arrival of the signal at the observer, and ϕ0 and f0 are an initial phase and frequency at the epoch t0 in a reference frame at rest with respect to the source, the time modulation term is Δτ(t). Assuming, for now, that the source is at rest with respect to the SSB, the time modulation can be expressed as a combination of terms (2) \begin{eqnarray} \Delta \tau = \Delta _{\rm R} + \Delta _{\rm E} - \Delta _{\rm S}, \end{eqnarray} where ΔR (the Roemer delay) is a geometric retardation term, ΔE (the Einstein delay) is a relativistic frame transformation term taking into account relativistic time dilation, and ΔS (the Shapiro delay) is the delay due to passing through curved space–time. These terms are discussed in, for example, chapter 5 of Lyne & Graham-Smith (1998), while Edwards, Hobbs & Manchester (2006) provide more detailed discussion of time delays accounting for more effects with particular relevance to pulsar observations. Here, for each of the terms we use the sign conventions given in the source code for the pulsar timing software tempo21 (Hobbs, Edwards & Manchester 2006) and in the LALSuite gravitational wave software library functions (LIGO Scientific Collaboration 2017), rather than those used in the equation of Edwards et al. (2006).2 The Roemer delay is given by (3) \begin{eqnarray} \Delta _{\rm R} = \frac{\boldsymbol {r}\cdot \hat{\boldsymbol {s}}}{c}, \end{eqnarray} where $\boldsymbol {r}$ is a vector giving the position of the observer with respect to the SSB, and $\hat{\boldsymbol {s}}$ is a unit vector pointing from the observer to the source. The Einstein delay (see e.g. equations 9 and 10 of Edwards et al. 2006) converts to a new time coordinate frame, and depends on the choice of frame you want, i.e. Barycentric Coordinate Time (TCB), in which the effect of the presence of the Sun's gravitational potential is removed. The Shapiro delay (for which we will only consider the contribution from the Sun) is to first order given by (4) \begin{eqnarray} \Delta _{\rm S} \equiv \Delta _{\rm S_{\odot }} = -\frac{2G {\rm M}_{\odot }}{c^3}\ln {\left({\boldsymbol {r}}_{\rm se}\cdot \hat{\boldsymbol {s}} + |\boldsymbol {r}_{\rm se}| \right)} \end{eqnarray} for waves passing around the Sun, where $\boldsymbol {r}_{\rm se} = \boldsymbol {r}_{{\oplus}} - \boldsymbol {r}_{\odot }$ is the vector from the centre of the Sun to the geocentre.3 Unlike electromagnetic waves, gravitational waves will pass through matter, and therefore a different term is required for a wave passing through the Sun, i.e. when $\left|\boldsymbol {r}_{\rm se}\right|^2 - \left(\boldsymbol {r}_{\rm se}\cdot \hat{\boldsymbol {s}}\right)^2 < R_{\odot }^2$ and $\boldsymbol {r}_{\rm se}\cdot \hat{\boldsymbol {s}} < 0$, giving (5) \begin{eqnarray} \Delta _{\rm S_{\odot }} &amp;=&amp; -\frac{2G {\rm M}_{\odot }}{c^3}\Bigg [\ln {\left(\boldsymbol {r}_{\rm se}\cdot \hat{\boldsymbol {s}} + \sqrt{R_{\odot }^2 + \left(\boldsymbol {r}_{\rm se}\cdot \hat{\boldsymbol {s}}\right)^2} \right)} \nonumber \\ &amp;&amp;- \,2\left(1 - \frac{\sqrt{\left|\boldsymbol {r}_{\rm se}\right|^2 - \left(\boldsymbol {r}_{\rm se}\cdot \hat{\boldsymbol {s}}\right)^2}}{R_{\odot }}\right)\Bigg]\!, \end{eqnarray} where R⊙ is the radius of the Sun.

2022ApJ...937...76W__Verdini_et_al._2015_Instance_1

Direct measures of cascade rates in turbulent systems often employ theoretical formulations related to Kolmogorov’s “4/5” law (Kolmogorov 1941b; Frisch 1995) and its variants, in which the inertial range cascade rate is related to a signed third-order structure function. This so-called exact law is derived from the fluid equations without appeal to dimensional analysis, assumptions about scaling behavior, or any ansatz concerning timescales; however, this law does require time stationarity, spatial homogeneity, the existence of an inertial range, and a finite dissipation rate. The original formulation for isotropic incompressible hydrodynamics has been extended to magnetohydrodynamics (MHD; Politano & Pouquet 1998a,1998b) and related models. The MHD version is frequently applied to in situ observations of plasma turbulence in the solar wind (Sorriso-Valvo et al. 2007; MacBride et al. 2008; Bandyopadhyay et al. 2020) to obtain cascade rates that inform theories of heating and acceleration of the solar wind (Osman et al. 2011), providing ground truth for related approximations in space physics (Vasquez et al. 2007). Frequently a major issue in these applications is the use of formulations derived assuming isotropy in turbulence that is actually anisotropic (Verdini et al. 2015), this being the typical case for solar wind and magnetosheath turbulence. Usually this potential inconsistency is disregarded in favor of extensive averaging, whenever possible. Another more practical limitation is the challenging requirement of a sufficient volume of data (Podesta et al. 2009), a kinematic and statistical issue further complicated by potential sensitivity to the tails of the probability distribution of the fluctuations (Dudok de Wit 2004). Taking these challenges into account, we note that the ability to extract cascade rates from observational data is of increasing importance due to the centrality of fundamental questions relating to heating and dissipation in space and astrophysical plasmas (e.g., Kiyani et al. 2015). Therefore, in the present study we revisit several related issues that are pertinent to the evaluation of third-order laws using single-point or multi-point measurements. We reexamine the issue of averaging by focusing on conditions for obtaining accurate results in both isotropic and anisotropic turbulence. The strategies we examine are implemented using data from three-dimensional (3D) MHD turbulence simulations. A motivation for this approach is that for such cases we have an unambiguous determination of the underlying turbulence symmetry as well as a straightforward method to quantify the absolute dissipation rate.

2017MNRAS.470.3882T__Breen_et_al._2015_Instance_1

A sample of 17 starless clump candidates has been selected from the Traficante et al. (2015a) as objects with Σ ≥ 0.05 g cm−2, mass ${{{M}}}\ge 300$ M⊙, bolometric luminosity over envelope mass ratio L/M ≤ 0.3 and very low dust temperature (T  15 K), indicative of very young stage of evolution (see Section 4), no (or faint) emission at 70 μm after visual inspection of each source and no counterparts in the MSX and WISE catalogues in correspondence of the Herschel dust column density peak. In addition, we checked for different masers emission associated with these clumps, as they are an indication of on-going star formation activity. We searched in the methanol multibeam survey (MMB; Green et al. 2009) and found no Class II CH3OH masers in the sources of our sample (Breen et al. 2015); from the MMB survey we also searched for hydroxyl (OH) masers at 6035 MHz (Avison et al. 2016), a transition often associated with high-mass star-forming regions, and also found no associations. We searched for CH3OH and OH masers using also the Arecibo surveys of Olmi et al. (2014), which is more sensitive than the MMB survey and it is targeted to identify weak masers associated with Hi-GAL high-mass objects. We found no CH3OH masers at distances less than 100 arcsec from the source centroids. We found one source, 34.131 + 0.075, with a weak OH maser (peak emission of 20 mJy, ≃3σ above the rms of the observations made with the Arecibo telescope, Olmi et al. 2014). Finally, we checked several surveys of water masers in the first quadrant (Merello et al., in preparation, and references therein) and found that only one source, 23.271 − 0.263, has a H2O maser association (at ≃3 arcsec from the source centroid), identified in the survey of Svoboda et al. (2016). Note that the source 18.787−0.286 is classified as starless in Traficante et al. (2015a) catalogue and has no maser associations, although it shows a 70 μm counterpart. The 70 μm source however is faint, with a peak emission of ≃60 mJy pixel−1 compared to a background of ≃130 mJy pixel−1. The clump follows all the other selection criteria and has very low dust temperature (T = 10.6 K, see Section 4), so we include it in the analysis. The clump embedded in the cloud SDC19.281−0.387, which follows the same criteria but it is not in the Traficante et al. (2015a) catalogue, was also included in our selection. The final sample of 18 clumps presented here contains some of the most massive 70 μm quiet clumps observed in the Galaxy.

2022MNRAS.515.2633P__Blandford_&_Königl_1979_Instance_1

Active galactic nuclei (AGN) have been discovered a century ago and still remain a hot topic of research. The theoretical predictions and the observational results suggest that the AGN have three main components namely, a central supermassive black hole (SMBH), an accretion disc around the SMBH, bipolar high relativistic jets of particles. The launching of the jets perpendicular to the accretion disc plane remains a long-standing puzzle to the astronomers. The GRMHD simulations have made some progress in understanding the launching of the jets and it is suggested to be the interplay between the accretion flow, magnetic field lines, and the SMBH (Mościbrodzka & Falcke 2013; Mościbrodzka et al. 2014). Simulations and theory together suggest that the magnetic field plays an important role in collimating the particles and giving them a jet like shape (Blandford & Königl 1979; Blandford & Payne 1982; Koide, Shibata & Kudoh 1998; Mościbrodzka, Falcke & Shiokawa 2016). The jets are believed to be highly relativistic and the process of particle acceleration inside the jets are still unclear. The AGNs are classified in various types under the AGN unification scheme developed by Urry & Padovani (1995). According to this scheme, the observer’s viewing angle with respect to jet axis divides the sources in various class such as quasars, seyfert galaxies, blazars, etc. In the case of blazars, the jet axis is within few degrees to the observer’s line of sight. Due to the direct view of the jet and because of the superluminal motion, blazars are among the brightest sources in the Universe. Blazars are classified in two main types namely flat-spectrum radio quasars (FSRQs) and the BL Lacertae (BL Lac) objects depending upon the presence or absence of optical emission lines in their spectra (Stickel et al. 1991; Weymann et al. 1991). Based on the location of the synchrotron peak, the BL Lac objects are further divided into three parts known as low BL Lac (LBL), intermediate BL Lac (IBL), and high BL Lac (HBL) by Padovani & Giommi (1995).

2022MNRAS.516.5874W__Haasteren_2017_Instance_1

An outlier is defined to be an anomalous event or observation that arises from a process that differs from the majority of the data generation. Outlier detection has always been an indispensable part of data analysis; contaminated data sets without proper outlier treatment can increase modelling uncertainties and produce misleading results. A common mitigation strategy is to model the data as a Gaussian mixture of inlier and outlier distributions with differing variance (Hogg, Bovy & Lang 2010; Vallisneri & van Haasteren 2017). For example, assume a model with an inlier data model, yi = μi + ϵi, where yi is the i-th observation, μi is the mean, and ϵi is Gaussian random noise with variance $\sigma _i^2$. We can then account for possible outlier contamination in the data collection by modelling such fluctuations with $\epsilon _\mathrm{out}\sim \mathcal {N}(0, \sigma _\mathrm{out}^{2})$, where our full data model now includes a latent outlier indicator zi for each observation, where zi = 1 for an outlier and 0 otherwise. This indicator is modelled with a certain prior outlier probability, θ, e.g. zi ∼ Bernoulli(θ). This data model is a mixture of two Gaussians with different mean and different variances, which can be expressed in the form (1)$$\begin{eqnarray} y_i = (1-z_i) \mu _i +(1-z_i) \epsilon _i + z_i \epsilon _\mathrm{out}. \end{eqnarray}$$Incorporating the outlier indication parameter zi allows us to assess the possibility of an individual measurement being an outlier and to formulate the likelihood as a mixture of two Gaussians that respectively model the inlier and outlier distributions: (2)$$\begin{eqnarray} p \left(y_{i} \mid \mu _{i}, z_{i}, \sigma _{i}, \sigma _{\mathrm{out }}\right)=\left\lbrace \begin{array}{l{@}{\quad}l}\mathrm{e}^{-\left(y_{i}- \mu _{i}\right)^{2} /\big(2 \sigma _{i}^{2}\big)} / \sqrt{2 \mathrm{\pi} \sigma _{i}^{2}}, &amp; \text{for } z_{i}=0 \\ \mathrm{e}^{-y_{i}^{2} /\big(2 \sigma _{\mathrm{out }}^{2}\big)} / \sqrt{2 \mathrm{\pi} \sigma _{\mathrm{out }}^{2}}, &amp; \text{for } z_{i}=1 .\end{array}\right. \\ \end{eqnarray}$$We use this model for the toy sine wave example in Section 2.3.

2020AandA...639A..88C__Chatzistergos_et_al._2019b_Instance_2

To overcome these limitations, in our previous paper (Chatzistergos et al. 2018b, Paper I, hereafter) we introduced a novel approach to process the historical and modern Ca II K observations, to perform their photometric calibration, to compensate for the intensity centre-to-limb variation (CLV, hereafter), and to account for various artefacts. By using synthetic data, we also showed that our method can perform the photometric calibration and account for image artefacts with higher accuracy than other methods presented in the literature. More importantly, we showed that, as long as the archives are consistent with each other, for example, they are centred at the same wavelength and employing the same bandwidth for the observations, the method can be used to derive accurate information on the evolution of plage areas without the need of any adjustments in the processing of the various archives (Chatzistergos et al. 2019b, Paper II, hereafter). In Paper II, we applied our method to 85 972 images from 9 Ca II K archives to derive plage areas and produce the first composite of plage areas over the entire 20th century. In particular, we analysed the Ca II K archives from the Arcetri, Kodaikanal (8-bit digitisation), McMath-Hulbert, Meudon, Mitaka, Mt Wilson, Rome/PSPT, Schauinsland, and Wendelstein sites. Five out of the nine analysed archives were amongst the most studied and prominent ones, while the remaining archives were from less studied data sources. There are, however, many other Ca II K archives that are available and still remain largely unexplored. These archives harbour the potential to fill gaps in the available plage series as well as to address inconsistencies among the various archives and within individual archives (e.g. change in data quality, or in the measuring instrument with time). Moreover, since the work presented in Paper II, more data from various historical and modern archives became available in digital form. In particular, historical data that have been made available in the meantime include those from the latest 16-bit digitisation of the Kodaikanal archive, Catania, Coimbra, Kenwood, Kharkiv, Kyoto, Manila, Rome, Sacramento Peak, and Yerkes observatories, as well as additional data from the Meudon and Mt Wilson archives. In this light, here we present results from the most comprehensive analysis to date of historical and modern Ca II K observations taken between 1892 and 2019 from 43 different datasets for the purposes of producing a composite plage area series.

2018AandA...616A..96R__Haardt_&_Madau_(2012)_Instance_1

As expected, the final luminosity (LV) of our model dwarfs strongly correlates with the shape of their formation histories. We divide our models into three categories dependent on their LV range. In the following, we will refer to them as sustained, extended and quenched. A few representative cases of each of these three categories are shown in Fig. 9. The strength of the UV-background heating is indicated by the dotted black curve. It represents the hydrogen photo-heating rate due to the UV-background photons following the model of Haardt & Madau (2012). (a)LV > 108 L⊙, sustained: the star formation rate of those massive and luminous dwarfs increases over 1 to 2 Gyr. This period is followed by a rather constant SFR plateau. These systems are massive enough to resist the UV-background heating and, once formed, to form stars continuously. This sustained star formation activity that lasts up to z = 0 results from the self-regulation between stellar feedback and gas cooling (Revaz et al. 2009; Revaz & Jablonka 2012). (b)106 L⊙ LV 108 L⊙, extended: in this luminosity range, the star formation is clearly affected by the UV-background. After a rapid increase, the star formation activity is damped owing to the increase of the strength of the UV-heating. However, at the exception of the h070 halo which is definitively quenched after 6.5 Gyr, the potential well of those dwarfs is sufficiently deep to avoid a complete quenching. The star formation activity extends to z = 0, however, at a much lower rate than the original one. (c)LV 106 L⊙, quenched: the potential well of those galaxies is so shallow that the gas heated by the UV - photons escape the systems. Star formation is generally rapidly quenched after 2 or 3 Gyr. Only halo h064 shows signs of activity up to 4 Gyr. Those galaxies may be considered as true fossils of the re-ionization in the nomenclature of Ricotti & Gnedin (2005). They are all faint objects with only old stellar populations.

2020AandA...638A..16T__Barnes_(2017)_Instance_2

Figure 12 shows the results of our tidal evolution calculations. The left panel of Fig. 12 shows the planetary rotational evolution of GJ 1148 b due to star–planet tides. After ~850 Myr, GJ 1148 b reaches a rotation period that is 2∕3 of the orbital period, and remains there with Prot = 27.5 d. During the integration the planetary semi-major axis and eccentricity are mostly unaffected. An asymptotic rotation period that is shorter than synchronous and 2/3 of the orbital period is expected for eb ≳0.24 in the constant Q tidal model (Goldreich & Peale 1966; Cheng et al. 2014). The time for GJ 1148 b to reach asymptotic rotation is inversely proportional to the initial Prot, as long as the initial Prot is much less than 27.5 d, and it depends on the other parameters of GJ 1148 b according to Eq. (3) of Barnes (2017) and Eq. (15) of Cheng et al. (2014). The rotational period of GJ 1148 b is thus very likely much longer than the orbital periods of the hypothetical exomoons, which could be dynamically stable only with orbital periods between 0.7 and 2 d. The right panel of Fig. 12 shows that the longer rotational period of GJ 1148 b (Prot = 27.5 d) leadsto strong orbital decay of the stable exomoon orbits due to tidal interactions with the planet. An exomoon eventually reaches the Roche limit where it is tidally disrupted by the gas giant. Not even one hypothetical “stable” exomoon in the context of Sect. 5.3.1 had survived this test. The maximum time a Mars-like exomoon could survive is ~55 M yr, while for Titan-like moons the maximum survival time is longer, ~255 M yr. The latter is longer by roughly the mass ratio of Mars to Titan, which can be understood from Eq. (2) of Barnes (2017) and Eq. (16) of Cheng et al. (2014). These timescales are optimistic since the orbital decay would start before the planet reaches the asymptotic spin state. In both cases the survival times are much shorter than the age of the system. Therefore, given the relatively fast orbital decay in the small stable region around the planet, we conclude that exomoons around GJ 1148 b are unlikely to exist.

2015MNRAS.454.2691M__Leitherer_et_al._1999_Instance_1

In order for simulations to play a role in improving our understanding of the formation and dynamics of the CGM, particularly given the complex, multiphase picture emerging from the latest observations (Tumlinson et al. 2011; Werk et al. 2014), the level of detail and sophistication in stellar feedback models must improve. In this work, we analyse the outflowing (and infalling) gas seen in the galaxies and CGM of the Feedback in Realistic Environments (FIRE) simulations,1 first presented in Hopkins et al. (2014). Unlike the subgrid recipes which involve kinetically prescribed decoupled winds and cooling-suppressed blastwaves, the FIRE simulations solve the ‘overcooling’ problem by explicitly modelling the radiation pressure, stellar winds, and ionizing feedback from young stars as taken directly from the population synthesis code starburst99 (Leitherer et al. 1999). These ‘early feedback’ mechanisms act before SNe, heating and stirring the surrounding ISM which is necessary to match conditions in star-forming regions such as Carina (Harper-Clark & Murray 2009) and 30 Dor (Lopez et al. 2011; Pellegrini, Baldwin & Ferland 2011). SNe are implemented by taking into account their energy and momentum input. When the cooling radius of SNe is resolved, SN energy injected is free to expand and generate momentum in the ISM before too much energy is radiated away. When this scale is poorly resolved, momentum accumulation from SN remnant evolution below the resolution scale is added to the surrounding gas. This model is physically realistic when it is applied on the scale of giant molecular clouds, meaning that a resolution of several to tens of parsecs is required. The physical feedback implementation in FIRE successfully regulates mass accumulation in galaxies and provides a physical explanation for the inefficiency of star formation in galactic discs (Kennicutt 1983, 1998; Genzel et al. 2010). We stress that we allow hydrodynamical interactions and cooling of all gas at all times, unlike in typical subgrid models. This is critical to make meaningful predictions for the phase structure of circumgalactic gas.

2021AandA...653A..36M__Goulding_&_Alexander_(2009)_Instance_4

The SFG sample was constructed using the Great Observatories All-Sky LIRG Survey (GOALS sample, Armus et al. 2009), from which we extracted 158 galaxies, with data from Inami et al. (2013), who report the fine-structure lines at high resolution in the 10 − 36 μm interval, and Stierwalt et al. (2014), who include the detections of the H2 molecular lines and the PAH features at low spectral resolution. For those galaxies in the GOALS sample that have a single IRAS counterpart, but more than one source detected in the emission lines, we have added together the line or feature fluxes of all components, to consistently associate the correct line or feature emission to the total IR luminosity computed from the IRAS fluxes. To also cover lower luminosity galaxies, as the GOALS sample only includes luminous IR galaxies (LIRGs) and ultra-luminous IR galaxies (ULIRGs), we included 38 galaxies from Bernard-Salas et al. (2009) and Goulding & Alexander (2009), to reach the total sample of 196 galaxies with IR line fluxes in the 5.5 − 35 μm interval in which an AGN component is not detected. For the Bernard-Salas et al. (2009), Goulding & Alexander (2009), and the GOALS samples, we excluded all the composite starburst-AGN objects identified as those with a detection of [NeV] either at 14.3 or 24.3 μm. It is worth noting that the original samples from Goulding & Alexander (2009) and Bernard-Salas et al. (2009) have spectra solely covering the central region of the galaxies. To estimate the global SFR, we corrected the published line fluxes of the Spitzer spectra by multiplying them by the ratio of the continuum reported in the IRAS point source catalogue to the continuum measured on the Spitzer spectra extracted from the CASSIS database (Lebouteiller et al. 2015). We assumed here that the line emission scales (at first order) with the IR brightness distribution. In particular, we considered the continuum at 12 μm for the [NeII]12.8 μm and [NeIII]15.6 μm lines, and the continuum at 25 μm for the [OIV]25.9 μm, [FeII]26 μm, [SIII]33.5 μm, and [SiII]34.8 μm lines. This correction was not needed for the AGN sample and the GOALS sample because of the greater average redshift of the galaxies in the 12MGS and GOALS samples. In particular, the 12MGS active galaxy sample has a mean redshift of 0.028 (Rush et al. 1993), while the GOALS sample has a mean redshift of 0.026. The galaxies presented by Bernard-Salas et al. (2009) have instead an average redshift of 0.0074, while the sample by Goulding & Alexander (2009) has an average redshift of 0.0044. For the other lines in the 10 − 36 μm interval, Goulding & Alexander (2009) did not report a detection, and we used the data presented in Bernard-Salas et al. (2009) for a total of 15 objects. Both Bernard-Salas et al. (2009) and Goulding & Alexander (2009) reported data from the high-resolution Spitzer-IRS spectra. Data in the 50 − 205 μm interval were taken from Díaz-Santos et al. (2017). For the GOALS sample, 20 starburst galaxies were taken from Fernández-Ontiveros et al. (2016), and 23 objects were taken from the ISO-LWS observations of Negishi et al. (2001). As a result, we obtained a total sample of 193 objects. Lastly, the PAH features’ fluxes were measured from the low-resolution Spitzer-IRS spectra by Brandl et al. (2006), including 12 objects from the sample of Bernard-Salas et al. (2009) and 179 objects from Stierwalt et al. (2014).

2019MNRAS.486.1781R__Bonning_et_al._2012_Instance_1

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

2020ApJ...898L..25K__Townsley_&_Bildsten_2003_Instance_1

Once He core burning finishes the core contracts and hydrogen burning starts, the radius of the hot subdwarf expands beyond its Roche radius, and mass transfer starts at an orbital period close to the observed orbital period. Mass transfer will continue for ≈1 Myr at a rate exceeding 10−9 M⊙ yr−1 until hydrogen shell burning is finished and the star contracts to become a carbon–oxygen WD with a thick helium layer and a small residual layer of hydrogen. The high accretion rate will heat the accreting WD significantly (Townsley & Bildsten 2003). Models predict a Teff ≈ 50,000 K for accretion rates of 10−9 M⊙ yr−1 (Burdge et al. 2019) consistent with the high blackbody temperature of the accretor observed in ZTF J2055+4651. Accretion onto the WD companion at this rate will cause unstable hydrogen ignition after ≈10−4 M⊙ accumulates, leading to classical novae eruptions (Nomoto 1982; Nomoto et al. 2007; Wolf et al. 2013). This accretion rate predicts a recurrence time of order 105 yr for a total of approximately 10 novae. Our binary model suggests that this system is within the first ≈10% of the 1 Myr accretion phase. At the current state the orbit will shrink with s s−1, which will be detectable after a few years of monitoring. The right panel of Figure 5 shows the evolution of the donor through this mass transfer phase. After accretion has ceased, the orbit of the system will continue to shrink due to the radiation of gravitational waves and the system will merge in ≈30 Myr. Our models predict that there is a substantial He layer of ≈0.05 M⊙ left in the former hot subdwarf and the total mass of the system is relatively high (Mtotal ≈ 1.1 M⊙). Recent models predict that such a system explodes as a subluminous thermonuclear supernova (Perets et al. 2019; Zenati et al. 2019). If the system avoids a thermonuclear supernova it will merge and could evolve into a rapidly rotating single high-mass carbon–oxygen WD (Saio 2008; Clayton 2012; Schwab 2019).

2018ApJ...866L...1S__Pecharromán_et_al._1999_Instance_1

It was found that the complex dielectric function from Pecharromán et al. (1999) for the sample obtained by heating bayerite at 1273 K, assuming a spheroid with depolarization parameters of (0.35, 0.003), produced an opacity with 11, 20, 28, and 32 μm features, so this component was included in the models. However, with only this component, the observed 20 μm features in the residual spectra were found to be wider than those in the models. By adding the opacity of the sample obtained by heating boehmite at 1173 K, the width of the 20 μm feature could be matched. This was done using the complex dielectric function for the sample obtained by heating boehmite at 1173 K from Pecharromán et al. (1999), assuming a spheroid with depolarization parameters of (0.35, 0.035). The complex dielectric functions of the samples obtained by heating bayerite and boehmite to various temperatures (Pecharromán et al. 1999) were derived by modeling the reflectance spectra of pellets obtained by pressing powders of these materials under great pressure. This method required Pecharromán et al. (1999) to assume an effective medium theory, such that a pellet is a mixture of one of their samples with a matrix of air. Pecharromán et al. (1999) noted that heating bayerite at 500°C eliminates the XRD pattern of bayerite, and they note that at 700°C, the infrared reflectance spectrum of the boehmite sample no longer shows OH− stretching bands. This must mean that the samples obtained from heating bayerite at 1273 K and from heating boehmite at 1173 K are no longer bayerite or boehmite, respectively. XRD performed by Pecharromán et al. (1999) of the sample of bayerite prepared at 1273 K suggests only θ-alumina was present, and their infrared and NMR spectroscopy confirms this. XRD of their sample obtained from heating boehmite to 1173 K (Pecharromán et al. 1999) suggests δ-alumina to be present, though some amounts of θ-alumina and α-alumina are present, as they deduce from XRD and infrared and NMR spectroscopy.

2018MNRAS.479.3254V__Schneider_et_al._2018_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).

2019MNRAS.483..971V__al_2002_Instance_1

We have also considered early radio emission from gamma-ray bursts (GRBs), which can have higher brightness temperatures at early times than blazars, owing to their ultrarelativistic velocities. They can therefore be brighter and easier to measure while still at small angular sizes, and are consequently observed to show interstellar scintillation in their first days at ${\sim } 5\, \mbox{GHz}$ (Granot & van der Horst 2014). Before deceleration to Lorentz factor Γ 1/θj (before the ‘jet break’ for a jet of opening half-angle θj), the projected source angular size θ at (earth) time T after explosion of a GRB at redshift $z$ is θ ∼ 2cT Γ/DM($z$), where DM($z$) = DA($z$)(1 + $z$) is the proper motion distance, and DA($z$) the angular diameter distance. The Blandford–McKee blast wave of the ultrarelativistic shock moving into a medium of uniform external density ρ0 has radius R ≃ 2cT Γ2/(1 + $z$) and explosion energy per unit solid angle E/Ω ≃ ρ0R3Γ2c2, which gives Γ ≃ 9(Eiso, 53/n0)1/8(T/[(1 + $z$)day])−3/8, where $E=10^{53}\, \mbox{erg}(\Omega /4\pi)E_{iso,53}$ and n0 is the external density in cm−3 (Granot et al 2002, cf.). At DM($z$ = 1) = 3.3 Gpc, $\theta = [0.2,\, 1,\, 4]\, \mu \mbox{as}$ at $T=[0.1,\, 1,\, 10]\, \mbox{d}$. Thus at $\lambda \lt 4\, \mbox{cm}$ (the transition wavelength below which Milky Way scintillation becomes unimportant), the GRB will be smaller than our fiducial scattering angle $\theta =20\,\mu \mbox{as}(\lambda /30\, \mbox{cm})^{11/5}\lt 0.25\,\mu \mbox{as}$ for less than 0.1 d. During this time, the scintillation time-scale will be set by the rapidly expanding source, expanding across the refractive screen at a projected speed of ∼ΓcDl/Ds. This is many times c for our cosmological lenses with Dl ∼ 0.5Ds (but less than $1\, \mbox{km s}^{-1}$ for Milky Way interstellar plasma at $D_l\sim 100\, \mbox{pc}$, so Milky Way scintillation time-scales are dominated by gas motions in the Milky Way, not the apparent source expansion). The refractive scintillation time-scale is thus the same as the time-scale for the source to expand to a size larger than the refractive scale – i.e. the source will have only about 1 speckle before becoming too large to display refractive scintillation. This would be difficult to convincingly detect in a GRB.

2016MNRAS.463L..26B__Ackermann_et_al._2014_Instance_1

We calculate the corresponding γ-ray spectrum constructed from the photons arriving at the observer within the observation time of MAGIC and plot it in the bottom panel of Fig. 5. Although it is possible to explain the very fast variability of the emission even with a moderate Lorentz factor of the blob, strong constraints are put by the level of the observed flux. If the emission region has a radius of Rb = 3 × 1014 cm, and is moving with a Lorentz factor of γb = 100, we require an energy density of ρE = 20 erg cm−3 (measured in the blob's frame of reference) to reproduce the flux observed by MAGIC and Fermi-LAT (see the bottom panel of Fig. 5). Note that such large values of the Lorentz factor of the emission region in the jet have been already postulated in terms of other models in order to explain extremely short flares observed in this source (Ackermann et al. 2014) or in the other sources, e.g. PKS 2155−304 (Aharonian et al. 2007). Such large Lorentz factors of the blob find also some observational support from the observations of the superluminal motion in PKS 1510−089 which represents similar type of blazar (Jorstad et al. 2005). We can estimate the power of the blob in the observer's reference frame on $L_{\rm blob} = \pi R_{\rm b}^2c\rho _E \gamma _{\rm b}^2\approx 1.7\times 10^{45}$ erg s−1. On the other hand, the Eddington luminosity of the black hole in PKS 1222+21, with the mass 6–8 × 108 M⊙, is LEdd = 1.3 × 1047M9 ≈ 8–10 × 1046 erg s−1. Therefore, the blob has to contain about ∼2 per cent of the Eddington power. This is quite demanding but seems not to be excluded, especially if Rb ≈ R⊥. Lower values of γb require a much higher energy density in the blob (e.g. ρE = 340 erg cm−3 for γb = 50 and Rb = 3 × 1014 cm). The strong dependence on the γb is a combined result of the transformation of the energy density to the reference frame of the observer and the beaming of the emission in a narrower cone. Note that a larger radius of the blob will lower the energy density constraint, e.g. for Rb = 1015 cm we obtain ρE = 4.9 erg cm−3 for γb = 100 and ρE = 80 erg cm−3 for γb = 50, at the assumption that there is no competing energy loss process of the electrons at such a large distance from the star.

2015ApJ...799...55G__Klassen_et_al._2000_Instance_1

While the angular extent of IP shocks can be directly investigated using multi-point in situ measurements, the size of coronal shocks can only be indirectly inferred via remote-sensing observations of the electromagnetic emissions associated with them. According to Nelson & Robinson (1975), the average angle subtended at the solar surface by fundamental metric type II radio emission sources is 43°. Aurass et al. (1994) found particular cases with larger, double type II source structures covering a separation angle beyond 90°. Type II radio sources often show non-radial propagation trajectories (see Mann et al. 2003, and references therein). Wave-like large-scale disturbances propagating over the solar disk in extreme ultraviolet observations (usually referred to as “EIT waves” or “EUV waves”) are in close empirical correlation with type II radio bursts (Klassen et al. 2000). Most EIT waves are accompanied by CMEs, and observations and MHD modeling suggest that they are driven by the lateral expansion of CMEs, while the ultimate nature of the phenomenon remains under debate and could consist of true waves, pseudo waves (e.g., reconnection fronts), or a combination of both (Patsourakos & Vourlidas 2012; Nitta et al. 2013b, and references therein). According to Patsourakos & Vourlidas (2012), EIT waves can reach distances up to 1.3 R (850 Mm) from the source. Single-case studies reported some EIT waves covering a whole solar hemisphere (Klassen et al. 2000; Kienreich et al. 2009; Thompson & Myers 2009). Connections between EIT waves and SEP events have been often suggested (e.g., Bothmer et al. 1997; Krucker et al. 1999), and recently Rouillard et al. (2012) hypothesized that the EIT wave can be used to track the expansion of a coronal shock responsible for particle acceleration. Other authors question the EIT wave acceleration scenario for SEPs, with many EIT waves being observed at well-connected positions having no associated SEP increase (Miteva et al. 2014).

2015AandA...582A.104R__Stix_2002_Instance_1

Thermal motions of atoms produce a Doppler broadening of spectral lines with a Gaussian profile. Other unresolved velocities of a random nature are usually described as a turbulence broadening with a Gaussian or Lorentzian profile (see Rutten 2003, for a detailed description). The instrumental broadening encompasses the broadening caused by the finite spectral resolution of the instrument and is commonly approximated by a Gaussian. When other line broadening mechanisms are negligible, the total line broadening is the convolution of the line broadening profiles for the thermal and turbulence motions as well as the instrumental profile (Sect. 10.5 in Böhm-Vitense 1989). In other words, the velocity equivalent of the observed line width, Wobs = c × Δλ/λ, at 1/e of the peak intensity results from instrumental, Doppler, and turbulence (non-thermal) broadenings by (1)\begin{equation} W_{\rm{obs}}^2 = \left(c\times \frac{\Delta\lambda}{\lambda}\right)^2 = W_{\rm{instrumental}}^2 + W_{\rm{Doppler}}^2 + W_{\rm{turbulence}}^2, \end{equation}Wobs2=c×Δλλ2=Winstrumental2+WDoppler2+Wturbulence2,assuming that all three line broadening components have a Gaussian profile (Eq. (4.17) of Stix 2002). The POLIS instrumental width is less than 2 km s-1 (Beck 2006; Rezaei 2008; Beck et al. 2013a), while that of the Echelle spectrograph is of the same order. Assuming a generic chromospheric temperature of 104 K, we estimate a turbulence velocity using Eq. (2), (2)\begin{equation} \label{eq:one} \Delta\lambda=\frac{\lambda}{c}\sqrt{2\,k_{\rm B}\,T/m+ W_{\rm{turbulence}}^2\,\,+\,\,W_{\rm{instrumental}}^2 }, \end{equation}Δλ=λc2 kB T/m+Wturbulence2  +  Winstrumental2,where Δλ is the observed line width, m is mass of the calcium atom, c the speed of light, and kB is the Boltzmann constant (Tandberg-Hanssen 1960). Using the measured width of Ca ii H & IR lines of 1.0 and 0.7 Å, respectively (Sect. 4), we estimate a turbulence velocity of about 45 km s-1 for Ca ii H and 24 km s-1 for Ca ii IR lines (we also note that the H1 minima of the EB profile is about 1 Å   wider than in the quiet Sun profile). The width of the emission peaks on either side of the Hα line (1 Å) amounts to a turbulence velocity of 15 km s-1. Attributing the line width to the temperature and the instrumental profiles (omitting the turbulence broadening), we get a temperature of about 5 × 105 K, which is too hot for chromospheric heights. An increased turbulence velocity as a function of height in the chromosphere is part of standard models, either in the quiet Sun or sunspots (Kneer & Mattig 1978; Vernazza et al. 1981; Lites & Skumanich 1982). Our measured values, however, are larger than the turbulence velocity in an average atmosphere. The estimated turbulence velocity changes from one profile to another, but the general result remains the same: the observed width of the emission peaks is far in excess of any instrumental or thermal Doppler profile and to the first order has to have a turbulent nature. The Doppler width of a calcium line at 1–2 × 104 K is only about 2–3 km s-1, far from the observed widths of >20  km s-1.

2017AandA...605A..20C__Lattanzi_et_al._(2015)_Instance_1

As mentioned above, molecular oxygen was used to calibrate the magnetic field applied. A total of 155 Zeeman components (for the three transitions considered) was measured, with the magnetic field varied from B = 2.3 G (Itot = 0.2 Amp) to B = 113.5 G (Itot = 10 Amp). Figure 5 shows the Zeeman spectrum for the N,J = 3, 2 ← 1, 2 transition when a magnetic field of 5.7 G is applied. The fit was carried out with the program described in the previous section, with the spectroscopic parameters and g factors fixed at the values of Yu et al. (2012) and Christensen & Veseth (1978), Evenson & Mizushima (1972), respectively; the only free parameter was the correction factor to be applied to the theoretical magnetic field (see Eq. (1)). The fit reproduces in a satisfactory manner the measured Zeeman components, with a standard deviation of 73 kHz (the uncertainty for the measured frequencies was in most cases set to 70 kHz). Moving to SO, for the seven transitions considered, a total of 353 Zeeman components were measured, with the magnetic field varied from B = 5.7 Gauss (Itot = 0.5 Amp) to B = 124.8 Gauss (Itot = 11 Amp), and fitted as described above. In the fitting procedure the spectroscopic parameters (i.e., the rotational, centrifugal distortion, and fine structure constants) were kept fixed at the values derived by Lattanzi et al. (2015). The g factors resulting from the fit are given in Table 4 together with the best-estimated values discussed above, while the complete set of the measured Zeeman components is available in the Supplementary Material. Alternative fits were carried out: In the first fit, the three g factors, gs, gl, and gr, were fitted. In a second step, three different fits were carried out by fixing one of the three g factors and fitting the other two. In the last fit, only gs was determined. We note that in the first fit, based on the comparison with theory, the gs value is overestimated and the gl value is underestimated; the two terms therefore seem to be correlated. For this reason, we performed the additional fits described above. We note that in all cases, gr is determined with a limited accuracy, that is, with a relative uncertainty of ~20%. We also note that, if we fix gs at the best computed value, a gl value in good agreement with theory is obtained. However, by fixing gl at the best estimate, the resulting gs is still slightly overestimated. Simulations using values of gs in the 2.0020−2.0030 range show that the Zeeman splittings only change by a few tens of kHz, that is, in most cases within the typical uncertainty affecting the frequency measurements. The last comment concerns the standard deviation of the fits that, in all cases, is about 50 kHz.

2021ApJ...915...93L__Magdziarz_&_Zdziarski_1995_Instance_1

Table 3 shows the best-fit model parameters for the baseline fit for the three observations. We note that we did not find any statistically significant neutral or ionized absorption intrinsic to the source. On the addition of an ionized absorption model generated using the latest atomic data with the photoionization modeling code CLOUDY (Ferland et al. 2013; Laha et al. 2016c, 2016b, 2017, 2019b), we did not detect any improvement in the fit, as also found in previous works on this source (Rivers et al. 2011). Two blackbody components were necessary to describe the soft excess. We detected a narrow FeKα emission line in the Suzaku observation at an energy E = 6.41 ± 0.01 keV, while a marginally broad line (σ = 0.35 ± 0.08 keV) was required in the XMM-Newton and NuSTAR observations. We detected a neutral reflection hump at energy E > 10 keV with Suzaku that was modeled with pexrav, with an improvement Δχ2 = 32 for one additional degree of freedom. The pexrav model assumes an exponentially cutoff power-law spectrum reflected from neutral material (Magdziarz & Zdziarski 1995). The output spectrum is the sum of the cutoff power law and the reflection component. However, the reflection component alone can be obtained by setting the relative reflection value negative. The power-law cutoff energy for pexrav is assumed to be 300 keV, the abundance of the reprocessor set to solar value, and we allowed the inclination angle of this model to vary between 0° and 45° (being a Seyfert 1 galaxy). We have tied the power-law photon index normalization of pexrav to that of the primary power-law component. The pexrav reflection fraction is 0.17 ± 0.03. Note that throughout the text, we refer to the positive value of the reflection fraction. We did not detect any significant neutral reflection component in the XMM-Newton+NuSTAR spectra. Table 2 shows the fluxes of the soft excess, power law, FeKα emission line, and hard X-ray excess. In XSPEC notation, the best-fit baseline model is written as constant × tbabs × (powerlaw+bbody+pexrav+Gaussian(s)). We note that we required an emission line at 0.56 keV for XMM-Newton observation, which corresponds to O vii emission and is found in several bright AGNs, and we did not need that model for Suzaku and NuSTAR, as they do not cover that energy range.

2015MNRAS.448..629G__Cenko_et_al._2011_Instance_1

The remaining six parameters are less constrained. They are ϵe, ϵB, n0, L (where L = ηL0), κ and Ek. We apply constraints to the range of allowed values for these parameters. ϵB has been found to be as low as 10−8 (Barniol Duran 2014; Santana, Barniol Duran & Kumar 2014) and as a fraction can be as high as 1. In practice, ϵe tends towards higher values than ϵB. We set an upper limit of 1, noting that ϵe actually refers to the electron population that is emitting synchrotron radiation, rather than the electron population as a whole, and set a lower limit of 10−3 (Kumar 2000). n0 is limited between 10−5 and 100 cm−3, in line with what has been found in these sources (Cenko et al. 2011). The upper limit of L is set by the argument in equation (5), and values of this parameter below ∼1047 erg s−1 are never energetic enough to match the data, so we set the lower limit as 1047 erg s−1. Within these limits for L, we find that EE ceases to have any influence on the light curve if κ ≲ 10−2. If EE is isotropic, and the observed luminosity is only 1 per cent of the true energy (i.e. the conversion efficiency of kinetic to potential energy in the internal shocks is 1 per cent), then the energy delivered to the synchrotron shock front could be up to 100 times higher than observed in the light curve. In practice, however, the emission is (a) unlikely to be fully isotropic, (b) likely to shock more efficiently than 1 per cent, and (c) certain to be less than 100 per cent efficient at delivering its energy to the synchrotron shock front. For these reasons, we set the upper limit of κ at a still fairly generous factor of 10. Finally, we limit the energy in the shock from prompt emission to 1048 erg Ek 1052 erg. The arguments for these limits are identical to those used for κ, except that the prompt emission is known to be beamed (Sari, Piran & Halpern 1999; Frail et al. 2001) so the upper limit is lower, and because the injected energy at early times is negligible, Ek dominates the early light curve so the lower limit can be much less energetic before its influence vanishes. These limits are summarized in Table 3.

2016ApJ...827..151Y__Couvidat_et_al._2015_Instance_1

The data produced by the HMI instrument are of high quality; however, there are known uncertainties, limitations and systematic errors present that affect the measurement of magnetic flux. These include a sinusoidal variation in the total magnetic flux with a periodicity of 12 and 24 hr, due to a Doppler shift present in the Fe spectral line (Hoeksema et al. 2014). The main contribution to this shift is the geosynchronous orbit of SDO. The daily variation of ±3 km s−1 in spacecraft orbital velocity causes a sinusoidal variation in the total flux measured. This affects weak and strong magnetic fields in the LoS magnetograms differently, with the daily variation remaining less than 30 G for field strengths below 1000 G and less than 75 G for field strengths below 2250 G. On average during a day this is roughly ±35 G (Couvidat et al. 2015). Nevertheless, the strength of these instrumental and observational effects does not account for the strong flux cancellation we observe in AR 11226 prior to eruption. The leading edge of the positive polarity of AR 11226 reaches ∼60° from central meridian on June 6. Due to the spatially dependent sensitivity of HMI the noise level increases as a function of the center-to-limb angle and the spacecraft’s orbital velocity. This increases the value of pixels in low and moderate fields (between 250 and 750 G) by a few tens of percent and manifests itself as broad peaks that are centered at ∼±60° in the magnetic flux. This could be responsible for the increase in flux seen in Figure 5 at this time. However, the increase also coincides with the emergence of the two anti-Hale bipoles and so it is difficult to disentangle these effects. The proximity of AR 11226 to the limb on June 6 means that the magnetic flux was not measured right up until the time of the eruption but only until approximately a day beforehand. Therefore, these results provide a lower limit to the total flux canceled in the lead-up to the eruption. Furthermore, due to the fact that cancellation persists for several days, we can extrapolate that, if the cancellation process continued at the same rate (2.0 × 1019 Mx hr−1) as we observed in the period from June 1 09:00 UT to June 5 00:00 UT up until eruption, then an extra 1.1 × 1021 Mx could have been canceled. This would therefore result in a total amount of 2.8 × 1021 Mx flux canceled between two consecutive CMEs.

2020AandA...644A..59K__Heays_et_al._2017_Instance_3

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.

2016MNRAS.457..212S__Ijjas,_Steinhardt_&_Loeb_2013_Instance_1

One of the first and simplest proposed Friedmann–Robertson–Walker (FRW) cosmological model is the Λ cold dark matter (ΛCDM) universe, which involves Einstein's cosmological constant Λ. This standard model of cosmology, which is also referred to as the concordance model, assumes that the total energy density ρ of the universe is made up of three components, namely matter ρm (baryonic and dark matter), radiation ρr, and dark energy or vacuum energy ρΛ, which produces the necessary gravitational repulsion. In this model, dark energy which has an equation of state (EOS) ωΛ = pΛ/ρΛ = −1, is a property of the space itself and its density ρΛ = −pΛ = Λc4/8πG is constant, such that as the universe expands the constant vacuum energy density will eventually exceed the matter density of the universe which is ever decreasing. The spatially flat ΛCDM model dominated by vacuum energy with ΩΛ ∼ 0.70, with the rest of the energy density being in the form of non-relativistic cold dark matter with Ωm ∼ 0.25 and non-relativistic baryonic matter with Ωb ∼ 0.05, fits observational data reasonably well (Riess et al. 1998; Permutter et al. 1999; Knop et al. 2003; Riess et al. 2004). However, the main problem in this model is the huge difference of about 10120 orders of magnitude between the observed value of the cosmological constant and the one predicted from quantum field theory; known as the cosmological constant problem (Weinberg 1989). Another issue is the so called coincidence problem which expresses the fact that although in this model the matter and dark energy components scale differently with redshift during the evolution of the universe, both components today have comparable energy densities, and it is unclear why we happen to live in this narrow window of time. Besides these main issues, there are other inherent problems faced by the ΛCDM, some of which arose as a result of recent observations that are in disagreement with the model's predictions. For example, in order to account for the general isotropy of the cosmic microwave background (CMB), the standard model invokes an early period of inflationary expansion (Kazanas 1980; Guth 1981; Linde 1982). However, the latest observations by Planck (Planck Collaboration XXIII 2003) indicate that there may be some problems with such an inflationary scenario (Ijjas, Steinhardt & Loeb 2013; Guth, Kaiser & Nomura 2014). It was partly due to these issues of the standard ΛCDM, that during the last decade several alternative dark energy models have been proposed and tested with observations. In these models the dark energy density component ρde is not constant and in most cases ωde = pde/ρde depends on time, redshift, or scale factor. For example in some of these so called dynamical dark energy models, late time inflation is achieved using a variable cosmological term Λ(t) (Ray et al. 2011; Basilakos 2015) sometimes taken in conjunction with a time dependent gravitational constant G(t) (Ray, Mukhopadhyay & Dutta Choudhury 2007; Ibotombi Singh, Bembem Devi & Surendra Singh 2013). Other sources of dark energy include scalar fields such as quintessence (Peebles & Ratra 2003), K-essence (Armendariz-Picon et al. 2001) and phantom fields (Singh, Sami & Dadhich 2003). An alternative approach to the dark energy problem relies on the modification of Einstein's theory itself such that in these alternative theories of gravity, cosmic acceleration is not provided solely by the matter side Tμν of the field equations, but also by the geometry of spacetime. These theories include the scalar-tensor theory with non-minimally coupled scalar fields (Barrow & Parsons 1997; Bertolami & Martins 2000), f(R) theory (Tsujikawa 2008), conformal Weyl gravity (Mannheim 2000) and higher dimensional theories such as the Randall–Sundrum (RS) braneworld model (Randall & Sundrum 1999), and the braneworld model of Dvali–Gabadadze–Porrati (DGP) (Dvali, Gabadadze & Porrati 2000). Over the last few years considerable interest has been shown in the simple FRW linearly expanding (coasting) model in Einstein's theory with a(t) ∝ t, H(z) = H0(1 + z). Like the ΛCDM the total energy density and pressure in this model are expressed in terms of matter, radiation and dark energy components, such that p = ωρ with ρ = ρm + ρr + ρde and p = pr + pde (since pm ≈ 0), but it includes the added assumption ω = −1/3, i.e. the cosmic fluid acting as the source has zero active gravitational mass. So this would definitely exclude a cosmological constant as the source of the dark energy component in this case. The model was first discussed by Kolb (1989) who referred to this zero active mass cosmic fluid as ‘K-matter’. Interest in this model has been revived recently after it was noted (Melia 2003) that in the standard model the radius of the gravitational horizon Rh(t0) (also known as the Hubble radius) is equal to the distance ct0 that light has travelled since the big bang, with t0 being the current age of the universe. In the ΛCDM this equality is a peculiar coincidence because it just happens at the present time t0. It was then proposed (Melia 2007, 2009; Melia & Shevchuk 2012) that this equality may not be a coincidence at all, and should be satisfied at all cosmic time t. This was done by an application of Birkhoff's theorem and its corollary, which for a flat universe allows the identification of the Hubble radius Rh with the gravitational radius Rh = 2GM/c2, given in terms of the Misner–Sharp mass $M = (4\pi /3)R_{{\rm h}}^{3}(\rho /c^2)$ (Misner & Sharp 1964). The added assumption of a zero active gravitational mass ρ + 3p = 0 implies (Melia & Shevchuk 2012) that Rh = ct or H = 1/t for any cosmic time t. This linear model became known as the Rh = ct universe. Unlike the ΛCDM/ωCDM which contains at least the three parameters H0, Ωm and ωde, the Rh = ct model depends only on the sole parameter H0, so that for example the luminosity distance used to fit Type Ia supernova data (Melia 2009) is given by the simple expression dL = (1 + z)Rh(t0)ln (1 + z). Also while the ΛCDM would need inflation to circumvent the well-known horizon problem, the Rh = ct universe does not require inflation. One should also point out that the condition Rh = ct is also satisfied by other linear models such as the Milne universe (Milne 1933), which however has been refuted by observations. Unlike the Rh = ct model discussed here, the Milne universe is empty (ρ = 0) and with a negative spatial curvature (k = −1). As a result of these properties its luminosity distance is given by $d_{L}^{\rm {Milne}} = R_{{\rm h}}(t_0)(1 + z)\sinh [\ln (1+z)]$, and it was shown that this is not consistent with observational data (Melia & Shevchuk 2012). In the last few years the Rh = ct universe received a lot of attention when it was shown (Melia & Maier 2013; Wei, Wu & Melia 2013, 2014a, 2014b, 2015; Melia, Wei & Wu 2015) that it is actually favoured over the standard ΛCDM (and its variant ωCDM with ω ≠ −1) by most observational data. This claim has been contested by Bilicki & Seikel (2012) and Shafer (2015) who argued that measurement of H(z) as a function of redshift and the analysis of Type Ia supernovae favoured the ΛCDM over the Rh = ct universe. However, this was later contested by Melia & McClintock (2015) who showed that the Rh = ct was still favoured when using model-independent measurements that are not biased towards a specific model. Others (see for example van Oirschot, Kwan & Lewis 2010; Lewis & van Oirschot 2012; Mitra 2014) have also criticized the model itself, particularly the validity of the EOS ω = −1/3 (Lewis 2013). These and other criticisms have been addressed by Bikwa, Melia & Shevchuk (2012); Melia (2012) (see also Melia 2015 and references therein.) As pointed out above the Rh = ct model would still require a dark energy component ρde, albeit not in the form of a cosmological constant. So the obvious question at this point would be: what are the possible sources for this component that together with the matter and radiation components will give the required total EOS, ω = −1/3? The purpose of this paper is to answer this question by discussing the various possible sources of dark energy that are consistent with this EOS. Since the radiation component ρr at the present time t0 is insignificant (at least for the ΛCDM with which this model has been compared) we assume that the total energy density ρ = ρde + ρm and the total pressure p = pde (pm ≈ 0), as is normally done in the other alternative dynamical dark energy models found in the literature. So in the next three sections we examine three possibilities for the source of dark energy in the Rh = ct model, namely a variable cosmological term Λ(t), a non-minimally coupled scalar field in Brans–Dicke theory which is equivalent to a variable gravitational constant G(t), and finally quintessence represented by a minimally coupled scalar field ϕ. We show that although the first two sources are consistent with the model, they are both unphysical, which leaves the third source of quintessence as the viable source of dark energy in the Rh = ct universe. Results are then discussed in the Conclusion. Unless otherwise noted we use units such that G = c = 1.

2022ApJ...936..102A__Williams_et_al._2006_Instance_3

As regards the modeling of BGK modes, there are two main theoretical approaches: the integral solution or BGK methodology and the differential (or Schamel) technique. In the former method (BGK), one assumes that the initial particle distribution function and the electrostatic potential profiles are known, so these are substituted into the Poisson equation and the integral equation is solved to obtain the trapped particle distribution function (Bernstein et al. 1957; Aravindakshan et al. 2018a, 2018b, and the references therein). In Schamel’s approach, the form of the trapped particle distribution function and of the passing (i.e., free, nontrapped) particle distribution function is assumed and substituted in Poisson’s equation, leading to a differential equation that is then solved to obtain the form of the potential (Schamel 1986; Luque & Schamel 2005, and the references therein). A distinguishing factor in the former (BGK) approach is that it involves a condition in the form of an inequality to be satisfied by the potential parameters (width and amplitude) in order for a BGK mode to be sustained. The BGK approach will be adopted in this work. The above models tacitly assume a collisionless electron-ion plasma. These assumptions are acceptable in the Earth’s magnetosphere. However, as we move farther from near-Earth plasma environments, the presence of charged dust in the plasma cannot be neglected. In the case of Saturn, there are observations of streaming ions by the Cassini spacecraft (Badman et al. 2012a, 2012b). We know that these streaming ion flows can lead to the generation of ion holes. Electrostatic solitary waves have been observed in Saturn’s magnetosphere (Williams et al. 2006) and in the dusty environment near its moon Enceladus (Pickett et al. 2015). Williams et al. (2006) reported observations of solitary structures in the vicinity of Saturn’s magnetosphere. They detected a series of bipolar pulses and speculated that these could be either electron holes or ion holes (Williams et al. 2006). Later on, Pickett et al. (2015) observed solitary wave pulses within 10 Rs (Rs is the Saturn radius) and near Enceladus. Near the Enceladus plume, they discussed how dust impacts affected the observed solitary waves. In fact, Pickett et al. (2015) pointed out that some of the bipolar electric field pulses associated with the solitary waves observed had an inverse polarity (i.e., a positive pulse first, followed by a negative pulse in a short time period) and suggested that this might be due to either an inverse direction of propagation or to a true inverse potential pulse polarity (sign). Moreover, Farrell et al. (2017) examined the conditions that allow low-energy ions, such as those produced in the Enceladus plume, to be attracted and trapped within the sheath of negatively charged dust grains. Using particle-in-cell simulations, they showed that with dust in the system, the large electric field from the grain charge disrupts pickup and leads to ion trapping. Their simulation results also reveal that the bipolar pulses reported in the Enceladus plume by Williams et al. (2006) and Pickett et al. (2015) could most probably be ion holes. In the light of the above information, we may suggest that the formation of ion holes is highly likely in the dusty plasma of environments such as the one found in Saturn. Importantly, the instrument on board Cassini, the Radio Plasma Wave Science instrument, does not have the ability to determine the polarity of the electric fields associated with the ESWs observed with 100% certainty as it lacks the ability to perform interferometry (Williams et al. 2006).

2022MNRAS.509.3488I__Terzić_&_Graham_2005_Instance_1

The values rinf, ρinf, and σinf of equations (9) and (10) were computed assuming a bulge mass profile. Unlike other works that use isothermal sphere or Dehnen profiles (see e.g Volonteri, Haardt & Madau 2003; Sesana 2010; Sesana & Khan 2015; Bonetti et al. 2018b; Volonteri et al. 2020), here we decided to use a Sérsic model. This choice is motivated by observational studies that found it to be a good approximation for fitting the bulge light distribution of different galaxies (Drory & Fisher 2007; Drory & Alvarez 2008; Gadotti 2009). The analytical expressions for the Sérsic model are taken from Prugniel & Simien (1997) (see also Terzić & Graham 2005): (11)$$\begin{eqnarray*} \rm \rho (\mathit {r}) = \rho _0 \left(\frac{\mathit {r}}{{\it R}_e}\right)^{-{\it p}} \mathit {\rm e}^{-{\it b}\left({\mathit {r}}/{{\it R}_e}\right)^{1/{\it n}}}, \end{eqnarray*}$$(12)$$\begin{eqnarray*} \rm \sigma ^2(\mathit {r}) &amp;=&amp; \frac{4 \pi G \rho _0^2 \mathrm{{\it R}_{\rm e}}^2 n^2 b^{2\mathit {n}(p-1)}}{\rho (\mathit {r})} \nonumber\\&amp;&amp;\times \, \int _Z^{\infty } \mathcal {Z}^{-\mathit {n}(p+1)-1} {\rm e}^{-\mathcal {Z}} \gamma (\mathit {n}(3-p),\mathcal {Z}) {\rm d}\mathcal {Z}, \end{eqnarray*}$$where $\rm {\it R}_e$ is the bulge effective radius,8 ρ0 is the central bulge density, and n is its Sérsic index. This index correlates with the central concentration of the bulge, being the bulges with smaller n, the ones less centrally concentrated. Finally, the variable γ represents the incomplete gamma function, whereas Z, p, and b are three different quantities that depend on the bulge properties: $\rm {\it Z} = {\it b}(\mathit {r}/{\it R}_e)^{1/{\it n}}$, $p = 1 - 0.6097 n^{-1} + 0.05563 n^{-2}$, and $\mathrm{\it b} = 2n - 0.33 + 0.009\,876 n^{-1}$. This Sérsic model causes that smaller MBHs spend more time in the hardening phase than the most massive ones. To guide the reader, for an MBHB system with total mass $\rm {\it M}_{bin} = 10^9\, M_{\odot }$, $q = 1$, and $e_{\rm BH} = 0.3$, the hardening time-scale is $\rm {\sim }0.2\, Gyr$. For the same system but with $\rm {\it M}_{bin} = 10^6\, M_{\odot }$, the time increases up to $\rm 10\, Gyr$. For further details, we refer to Biava et al. (2019), where a detailed study of hardening time-scales in different bulge profiles was performed.

2022MNRAS.515...71S___2017_Instance_1

Steady and smooth decline: Since falling from the peak of the 2012b event, SN 2009ip has only continued to fade and is now the faintest it has ever been in the optical. Immediately after the 2012 event (around day 200), it was declining somewhat slower than the rate of 56Co decay for a 56Ni mass of 0.04 $\, {\rm M}_{\odot }$ (shown by the dashed grey line in Fig. 2). During that time, its light curve and spectral properties were consistent with those of late-time SNe IIn, and it had an underlying broad-line spectrum similar to that of SN 1987A (Graham et al. 2014; Smith et al. 2014). Up to about day 1000 it continued to fade smoothly at a slower rate around 0.003 mag d−1, and spectroscopically it continued to resemble late-time interaction in SNe IIn (Fox et al. 2015; Smith et al. 2016b; Graham et al. 2017). Our new HST photometry shows that from about day 1000 to day 3000, SN 2009ip has continued to fade smoothly and steadily in the optical, at an even slower rate. While the HST cadence is obviously sparse at these late times, the filters with more than two observations (F555W and F814W) show no significant deviation from a steady decline; there is no evidence of any rebrightening or irregularity in the fading rate. From 2015 to 2021, the decline rates in the various optical filters are 0.00051 ± 0.00001 mag d−1 in F555W, 0.00068 ± 0.00002 mag d−1 in F606W, 0.00092 ± 0.00001 mag d−1 in F657N, and 0.00050 ± 0.00001 mag d−1 in F814W. (The UV is an exception, as discussed below.) While this decline is much slower than radioactive decay, such slow decline rates are not at all unusual for SNe IIn with late-time CSM interaction. SNe IIn span a wide diversity of late-time decay rates, ranging from some SNe IIn that have essentially flat light curves for many years, like SN 2005ip (Smith et al. 2009, 2017; Stritzinger et al. 2012; Fox et al. 2020), down to those that have only weak CSM interaction and faster decline rates that are difficult to distinguish from radioactive decay or light echoes. Some SNe IIn even rebrighten at late times, like SN 2006jd (Stritzinger et al. 2012). In any case, it seems as if SN 2009ip is levelling off and approaching a constant V absolute magnitude of −7.5 or so. In all three broad continuum filters, the object is now fainter than the progenitor, and the light curve has not exhibited any additional eruptive variability since the 2012b event.

2019AandA...627A.130D__Broadhurst_et_al._2019_Instance_2

Gravitational-wave astronomy has recently become a reality with the first detection of gravitational waves (GW hereafter) by the Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo ground-based interferemeters. To date, eleven events have been reported by the LIGO and Virgo detectors (Abbott 2018), and this number will quickly increase to tens of events in the coming years. Some of these events may correspond to gravitationally lensed events with magnification factors ranging from a few tens to a few hundreds (Dai et al. 2017; Ng et al. 2018; Li et al. 2018a; Smith et al. 2018a,b; Broadhurst et al. 2019). Recent works have studied lensing effects in the existing LIGO/Virgo O1 and O2 events (Hannuksela et al. 2019; Broadhurst et al. 2019), while Smith et al. (2018c) searched for candidate galaxy cluster lenses for the GW170814 event. The most likely lenses for such events would be massive galaxies or galaxy clusters (Ng et al. 2018; Dai et al. 2017; Smith et al. 2018b; Broadhurst et al. 2019). On the other extreme of the lens mass regime, compact objects with masses of a few hundreds to a few tens M⊙ can also act as lenses (Lai et al. 2018). In this case, the geometric optics limit is not valid since the Schwarszchild radius of the lens is comparable to the wavelength of the wave. For these relatively low masses, the lensing effect has a modest impact on the average magnification, but it can introduce a frequency dependence on the magnification (see for instance, Jung & Shin 2019; Lai et al. 2018). An even smaller mass regime was considered in Christian et al. (2018) where the authors find that lenses with a mass as low as 30 M⊙ could be detected with current experiments. They also consider future, higher-sensitivity experiments and show how they can push the limit to even smaller masses of order 1 M⊙. These conclusions are, however, obtained assuming isolated microlenses and without accounting for the effect of the macromodel, or other nearby microlenses. In the small mass regime, microlenses such as neutron stars have been also considered as scattering sources of GWs, and it is found that a GW can be focussed at a focal point near the neutron star surface (Halder et al. 2019; Stratton & Dolan 2019).

2019MNRAS.482..194B__Mallmann_et_al._2018_Instance_1

Observationally, it is a big challenge to conclude whether AGN’s feedback could regulate star formation or not, and how it regulates star formation. On one hand, if strong outflows emerge, they could clear out the star-forming gas to suppress star formation (Alexander & Hickox 2012; García-Burillo et al. 2014; Alatalo et al. 2015; Hopkins et al. 2016; Wylezalek & Zakamska 2016). The heating by jets propagating through the galaxies could prevent gas from cooling and cut-off the gas supply for further star formation (Karouzos et al. 2014; Choi et al. 2015). On the other hand, the outflows and jets interact with the gas in host galaxies and compress it to trigger new star formation (Silk 2013; Zubovas et al. 2013; Zubovas & Bourne 2017). In fact observations show either no or positive relationships between star formation rates and SMBH accretion rates but no negative trends are seen (Shi et al. 2007, 2009; Baum et al. 2010; Xu et al. 2015; Zhang et al. 2016; Mallmann et al. 2018). Whether AGN’s feedback plays the role may also depends on the spatial scale that observations could resolve and time-scale that the observed tracers could probe (Harrison et al. 2012; Cresci et al. 2015; Feruglio et al. 2015). For example, radiation from AGNs nearly instantaneously impact the surrounding ISM while the attenuation from ISM probably limits their impact to the nuclear regions (Roos et al. 2015). Outflows or jets travel slowly and may be decelerated after interactions with ambient gas, which delays their effects on star formation at large distances from the nuclei (Harrison 2017; Harrison et al. 2018). Feedback by jets or outflows on ISM also depends on their orientation relative to the dusty torus, making their effects on star formation to be anisotropic. The short duty cycle of AGNs could also make feedback by radiation from AGNs temporally variable in strength. Case studies of individual AGNs find evidence of coexistence of positive and negative feedback on star formation (Zinn et al. 2013), suggesting the complicated nature of AGN feedback.

2019MNRAS.487..845B__Acero_et_al._2015_Instance_1

Fermi–LAT (Atwood et al. 2009) is a pair conversion telescope, with a field of view (FoV) of above 2 sr, operating in the energy range from 20 MeV to 300 GeV. It is the most sensitive instrument available in this energy range (Ackermann et al. 2012). A few months after the launch in 2008 June, Fermi–LAT started to operate in all sky survey mode. The telescope scans the whole sky in 3 h (Atwood et al. 2009). For this paper we have analysed the Pass81 data from 2010 February 13 to 17. We analysed the data for Mrk 421 using the latest Fermi Science Tool2 software package version v10r0p5. In order to determine the flux and the spectrum of the source, maximum likelihood optimization has been used (Abdo et al. 2009). The data selection and quality checks have been made using the gtselect tool.3 Since the telescope is sensitive to γ-rays from the interactions of cosmic rays with the ambient matter, we set our maximum zenith angle at 105 deg to remove the background γ-ray events from Earth’s limb. The analysis included all photons from a circular region of 10 deg around Mrk 421, which we call the region of interest (ROI). Only photons of energy above 100 MeV were considered for further analysis. The latest LAT instrument response function ‘P8R2_SOURCE_V6’ has been used. The third Fermi–LAT catalogue (3FGL catalogue: Acero et al. 2015) has been used to include the contributions of sources inside the ROI. The spectral model of the source has been considered as a simple power law of the form $\mathrm{ d}N(E)/\mathrm{ d}E = N_0 (E/E_0)^{-\Gamma _{\mathrm{ ph}}}$, where N0 is called the prefactor, Γph is the index, and E0 is the scale in energy. The spectral parameters of the sources including Mrk 421 inside the ROI are kept free, whereas the spectral parameters of the sources beyond 10 deg from Mrk 421 are kept fixed to the values according to the 3FGL catalogue. The unbinned likelihood4 method has been used in order to estimate the detection significance of the sources. The test statistics parameter determined from the aforementioned method is given by TS = 2Δlog(L), where L denotes the likelihood function between the model with the source and without the source. According to the definition TS = 9 corresponds to a detection significance of ∼3σ (Mattox et al. 1996). All the sources with TS 9 are excluded from the likelihood analysis. In order to model the spectrum of sources, we used the latest Galactic diffuse emission model ‘gll_iem_v06’ and the isotropic background model ‘iso_P8R2_SOURCE_V6_v06’. We estimated the corresponding butterfly plots for the source using the methods mentioned in the Fermi–LAT webpage.5 VERITAS observed the high-flux state of Mrk 421 for ∼6 h. In order to obtain quasi-simultaneous data with VERITAS, we also analysed the Fermi–LAT data for this state by taking 12 h around the VERITAS observation period. In Fig. 2(d), we show the butterfly plots for both 24 and 12 h by closed butterfly (grey) and open butterfly with cross at the edges (blue), respectively, for the high-flux state.

2020ApJ...903L..22T__Vuitton_et_al._2007_Instance_2

While the Loison et al. (2015) CH3C3N model corroborates the upper atmospheric abundance of C4H3N inferred by Vuitton et al. (2007) from the T5 INMS measurements (a factor of 2 higher than those derived from T40 in Vuitton et al. 2019), a large disparity between the photochemical models (and within the ensemble of models produced by Loison et al. 2015) arises in the lower atmosphere due to the poorly constrained C4H3N branching ratios and reaction rate coefficients at temperatures appropriate for Titan. Aside from electron dissociative recombination of C4H3NH+ (Vuitton et al. 2007), neutral production of CH3C3N can occur in a few ways, as found through crossed beam experiments and theoretical and photochemical modeling studies (Huang et al. 1999; Balucani et al. 2000; Zhu et al. 2003; Wang et al. 2006; Loison et al. 2015). First, through the reactions of larger hydrocarbons with CN radicals, 1 2 Similarly, with CCN radicals following their formation through H + HCCN (Takayanagi et al. 1998; Osamura & Petrie 2004) and subsequent reactions with ethylene, 3 or through the chain beginning with acetylene, 4 While both reactions (3) and (4) are found to be equally likely by Loison et al. (2015), the production of CCN via H + HCCN is not well constrained, and the synthesis of CH3C3N through CN radicals (Equations (1) and (2)) are not included in their photochemical model. Additionally, cyanoallene may be produced through reactions (1)–(4) instead of (or in addition to) methylcyanoacetylene. CH3C3N itself may form the protonated species, C4H3NH+, through reactions with the HCNH+ and C2H5+ ions producing HCN and C2H4, respectively (Vuitton et al. 2007). The other mechanism for forming C4H3NH+ is through the combination of HCN and l-C3H3+, though the reaction rate coefficient for this reaction and the abundance of l-C3H3+ are unknown (Vuitton et al. 2007). As such, the production and loss pathways for both C4H3NH+ and CH3C3N require further investigation.

2020MNRAS.493L..98S__Sourie,_Oertel_&_Novak_2016_Instance_1

To solve equations (9)−(11), the pulsar rotation rate Ω0, the long-term spin-down rate $\dot{\Omega }_\infty$, the initial lag δΩ0, the mutual-friction coefficients $\mathcal {B}_{\operatorname{\text{f}}}$ and $\mathcal {B}_{\operatorname{\text{pin}}}$, and the ratios $I_{\operatorname{\text{n}}}^{\operatorname{\text{f}}}/I$ and $I_{\operatorname{\text{n}}}^{\operatorname{\text{pin}}}/I$ need to be specified. In what follows, Ω0 and $\dot{\Omega }_\infty$ are directly taken from pulsar timing. The coefficient $\mathcal {B}_{\operatorname{\text{f}}}$ in the non-pinned region is given by $\mathcal {B}_0$, and the corresponding drag-to-lift ratio by equation (8). In the pinned region, the coefficient $\mathcal {B}_{\operatorname{\text{pin}}}$ is given by equation (6), with the prescription (7) and suitable parameters. Typical values for the underlying parameters are: $\varepsilon _{\operatorname{\text{p}}}^{\operatorname{\text{pin}}}\simeq 0.05-0.2$, $\varepsilon _{\operatorname{\text{p}}}^{\operatorname{\text{f}}}\simeq 0.1-0.5$ (see e.g. Chamel & Haensel 2006; Sourie, Oertel & Novak 2016), $x_{\operatorname{\text{p}}}^{\operatorname{\text{pin}}} \simeq 0.05 - 0.1$, $x_{\operatorname{\text{p}}}^{\operatorname{\text{f}}} \simeq 0.05 - 0.4$, $\rho _{\operatorname{\text{pin}}} \simeq (0.5-2) \rho _0$, and $\rho _{\operatorname{\text{f}}} \simeq (2-6) \rho _0$, ρ0 ≃ 2.7 × 1014 g cm−3 being the nuclear saturation density (see e.g. Pearson et al. 2018). The ratios $I_{\operatorname{\text{n}}}^{\operatorname{\text{pin}}}/I$ and $I_{\operatorname{\text{n}}}^{\operatorname{\text{f}}}/I$ are computed using the relations $I_{\operatorname{\text{n}}}^{_X} /I^{_X} = 1-x_{\operatorname{\text{p}}}^{_X}$ (assuming uniform densities in each region), where $I^{_X}$ is the total moment of inertia of region X, and $I^{\operatorname{\text{f}}} = I-I^{\operatorname{\, \text{cr}}}-I^{\operatorname{\text{pin}}}$, $I^{\operatorname{\, \text{cr}}}$ denoting the crustal moment of inertia of the star. Typical values are: $I^{\operatorname{\, \text{cr}}}/I\simeq 0.01-0.05$ (Delsate et al. 2016) and $I^{\operatorname{\text{pin}}}/I\sim 0.05$ (Gügercinoğlu & Alpar 2014). Unlike the previous quantities, both the initial lag δΩ0 and number $N_{\operatorname{\text{p}}}$ of pinned fluxoids are essentially unknown. As shown in the next section, the large range of possible values for $N_{\operatorname{\text{p}}}$ could account for the very different spin-up behaviours in the Crab and Vela pulsars.

2021MNRAS.505.2111L__Xu_et_al._2018_Instance_1

Recently, quasars observed with multiple measurements, another potential cosmological probe with a higher redshift range that reaches to z ∼ 5, is becoming popular to constrain cosmological models in the largely unexplored portion of redshift range from z ∼ 2 to z ∼ 5. A sample that contains 120 angular size measurements in intermediate-luminosity quasars from the very long baseline interferometry (VLBI) observations (Cao et al. 2017a,b) has become an effective standard ruler, which have been extensively applied to test cosmological models (Li et al. 2017; Melia et al. 2017; Qi et al. 2017; Zheng et al. 2017; Xu et al. 2018; Ryan, Chen & Ratra 2019), measuring the speed of light (Cao et al. 2017a, 2020a), exploring cosmic curvature at different redshifts (Cao et al. 2019; Qi et al. 2019), and the validity of cosmic distance duality relation (Zheng et al. 2020). Then, Risaliti & Lusso (2019) put forward a new compilation of quasars containing 1598 quasi-stellar object (QSO) X-ray and ultraviolet (UV) flux measurements in the redshift range of 0.036 ≤ z ≤ 5.1003, which have been used to constrain cosmological models (Khadka & Ratra 2020b) and cosmic curvature at high redshifts (Liu et al. 2020a,c), as well as test the cosmic opacity (Geng et al. 2020; Liu et al. 2020b). Making use of this data to explore cosmological researches mainly depends on the empirical relationship between the X-ray and UV luminosity of these high-redshift quasars proposed by Avni & Tananbaum (1986), which leads to the Hubble diagram constructed by quasars (Risaliti & Lusso 2015, 2017; Lusso & Risaliti 2016; Bisogni, Risaliti & Lusso 2017). In general, the advantage of these two QSO measurements over other traditional cosmological probes is that QSO has a larger redshift range, which may be rewarding in exploring the behaviour of the non-standard cosmological models at high redshifts, providing an important supplement to other astrophysical observations and also demonstrating the ability of QSO as an additional cosmological probe (Zheng et al. 2021).

2022AandA...666A..67P__Gall_et_al._2017_Instance_1

The KN coincident with GW170817 showed a rapidly fading EM transient in the optical and infrared bands. The term kilonova was historically coined from the perception of its brightness being a thousand times larger than a nova (Li & Paczyński 1998; Rosswog 2005; Metzger et al. 2010). We discuss in this work that, even if far from such a standardized luminosity, clear dependencies on physical BNS quantities can be extracted. For AT 2017gfo, the observed spectral evolution is suggested to arise from a mixed composition of r-process elements in the ejected material (Gillanders et al. 2022); while early-time spectra are consistent with light r-process elements (nuclear masses A ∼ 90 − 140), later spectra require intermediate composition, producing even heavier elements such as lanthanides. An alternative scenario, such as dust formation in the KN that could also explain such a blue-red evolution, has been ruled out (Gall et al. 2017). In addition, a broad feature observed at ∼0.7 − 1 μm has been interpreted as due to Sr II (Watson et al. 2019; Domoto et al. 2021; Gillanders et al. 2022). Since then, no other KN has been firmly detected (see e.g., Coughlin 2020 and references therein for a summary of the follow-up efforts during LIGO and Virgo’s third observing campaign). Another merger candidate is linked to GW190814, located farther away at ∼240 Mpc, whose origin remains uncertain. However, it lacks of any EM counterpart and could likely be caused by a binary black hole merger (Essick & Landry 2020; Tews et al. 2021). GW190425 was the first BNS candidate in O3. The LALInference localization pipeline (Veitch et al. 2015) provided a 90% credible region of 7461 deg2, estimated luminosity distance of 156 ± 41 Mpc, much larger than that for GW170817, which complicated the search campaign resulting in no EM counterpart identified. The inferred merger parameters from the GW analysis published in Abbott et al. (2020a) for chirp mass 1 . 44 − 0.02 + 0.02 M ⊙ $ 1.44^{+0.02}_{-0.02}\,M_{\odot} $ are the following. Total mass M 1 + M 2 ≃ 3 . 4 − 0.1 + 0.3 M ⊙ $ M_{1}+M_{2} \simeq 3.4_{-0.1}^{+0.3}\,M_{\odot} $ , with M1 ∈ (1.62, 1.88) M⊙, M2 ∈ (1.45, 1.69) M⊙, and Λ ∼ ≤ 600 $ \tilde{\Lambda} \leq 600 $ for low spin prior, or M1 ∈ (1.61, 2.52) M⊙, M2 ∈ (1.12, 1.68) M⊙, and Λ ∼ ≤ 1100 $ \tilde{\Lambda} \leq 1100 $ for high-spin prior. Note that for the other confirmed BNS event, GW170817, the inferred total mass is M 1 + M 2 ≃ 2 . 73 − 0.01 + 0.04 M ⊙ $ M_{1}+M_{2} \simeq 2.73_{-0.01}^{+0.04}\,M_{\odot} $ with M1 ∈ (1.36, 1.60) M⊙, M2 ∈ (1.16, 1.36) M⊙, and Λ ∼ = 300 − 230 + 420 $ \tilde{\Lambda} = 300_{-230}^{+420} $ for low-spin prior (Abbott et al. 2019).

2019ApJ...881...42J__Kelson_et_al._2000_Instance_1

Stellar population evolution studies beyond z ≈ 1 have primarily focused on ages through studies of luminosity changes. Beifiori et al. (2017) used new data for 19 galaxies in z = 1.3–1.6 clusters obtained with the Very Large Telescope/KMOS to extend the redshift coverage of the results regarding the evolution of the mass-to-light (M/L) ratios of bulge-dominated passive galaxies. The authors used their new results together with the available literature results covering up to z = 1.3 (van Dokkum & Franx 1996; Jørgensen et al. 1999, 2006, 2014; Kelson et al. 2000; Wuyts et al. 2004; Holden et al. 2005, 2010; Barr et al. 2006; van Dokkum & van der Marel 2007; Saglia et al. 2010; Jørgensen & Chiboucas 2013) and low-redshift reference data for the Coma cluster (Jørgensen 1999; Jørgensen et al. 2006) to further solidify the evidence supporting passive evolution and a formation redshift zform ≈ 2. The formation redshift should be understood as the epoch of the last major star formation episode. At z ≈ 1 the massive (Mass > 1011 M) bulge-dominated galaxies in clusters appear to be in place and mostly passively evolving. Lower mass galaxies may still be added to the red sequence and from then on passively evolve (e.g., Sánchez-Blázquez et al. 2009; Choi et al. 2014), but see also Cerulo et al. (2016) for results supporting that the red sequence well below L⋆ is fully populated in rich clusters already at . Ultimately, the properties of galaxies mapped over a large fraction of the age of the universe, may constrain the models for building the galaxies. It is difficult to understand within the prevailing hierarchical model favored by the ΛCDM (cold dark matter) cosmology, the existence of such massive passive galaxies with relatively old stellar populations at z ≈ 1, while less massive galaxies appear to harbor younger stellar populations, e.g., Jørgensen et al. (2017, and references therein), see Kauffmann et al. (2003) for a discussion of this tension between the observational results and the hierarchical models of galaxy formation. However, more recent cosmological simulations like Illustris (Genel et al. 2014; Vogelsberger et al. 2014; Wellons et al. 2015) and UniverseMachine (Behroozi et al. 2019) find that massive quiescent galaxies can be in place by z ≳ 2.

2022ApJ...928...38T__Tiwari_&_Nusser_2016_Instance_1

The LoTSS survey, homogeneously covering the whole northern sky complete down to the sub mJy limit will overcome statistical limitations due to shot noise. The large galaxy number density and large sky coverage will substantially reduce cosmic variance in cosmological analysis. The radio galaxies, tracing the background dark matter, will constrain the shape of power spectrum, i.e., the early universe physics, dark matter, baryon, density of neutrinos, the inflation power spectrum, and the degree of non-Gaussianity in density fluctuations. The upcoming LoTSS catalogs, covering a large sky area, will help us to explore further regarding large-scale anomalies (de Oliveira-Costa et al. 2004; Ralston & Jain 2004; Schwarz et al. 2004; Tiwari & Aluri 2019) and the current puzzling dipole signal observed with radio catalogs (Blake & Wall 2002; Singal 2011; Gibelyou & Huterer 2012; Rubart & Schwarz 2013; Tiwari & Jain 2015; Tiwari et al. 2015; Tiwari & Nusser 2016; Colin et al. 2017; Siewert et al. 2021). Furthermore, LoTSS will significantly improve on present low-frequency radio catalogs, e.g., TIFR GMRT Sky Survey (TGSS; Intema et al. 2017) and GaLactic and Extragalactic All-sky MWA (GLEAM; Hurley-Walker et al. 2017), and analyses based on these surveys (Dolfi et al. 2019; Rana & Bagla 2019; Tiwari 2019; Tiwari et al. 2019; Choudhuri et al. 2020). Unfortunately, the link between the galaxy and total matter power spectra depends on some unknowns from astrophysics, such as the galaxy bias factor, which depends on galaxy type and is quite different for radio AGNs and star-forming galaxies. The LoTSS population is a mixture of AGNs and star-forming galaxies, and therefore understanding galaxy bias, relative number densities, and luminosity evolution is nontrivial. The purpose of this work is to present a detailed cosmological analysis of LoTSS galaxies and study the effect of survey footprint, shot noise, and other systematics. We have produced galaxy mocks for the survey and have customized and calibrated the data pipeline for galaxy clustering statistics recovery.

2022MNRAS.510.4723P__Henri_&_Petrucci_1997_Instance_1

A new result brought by our work is the role of incident polarization on the reflected spectral outcome. Although some X-ray polarization of the primary radiation is expected to be due to Comptonization of thermal radiation inside the corona, general estimates for the polarization degree of the primary source are rather low ($\lesssim 10\,{\rm {per \, cent}}$) according to the recent analysis in Beheshtipour et al. (2017), Tamborra et al. (2018), and Beheshtipour (2018). If some polarization was present, then, for symmetric reasons, vertically or horizontally polarized light should be dominant in case of extended coronal models (Dabrowski & Lasenby 2001; Niedzwiecki & Zycki 2008; Schnittman & Krolik 2010) at low heights. In case of lamp-post models (Matt, Perola & Piro 1991; Martocchia & Matt 1996; Henri & Petrucci 1997; Martocchia, Karas & Matt 2000; Dovčiak, Karas & Yaqoob 2004a; Miniutti & Fabian 2004) general-relativistic effects will rotate the polarization position angle along null geodesics from the corona towards the disc (Connors et al. 1980) and the situation becomes more general for incident disc irradiation, i.e. any incident state of polarization is possible. Our computations, apart from the unpolarized case, assumed two extreme cases of initial $p = 100\,{\rm {per \, cent}}$ polarization in order to estimate its possible effects in comparison with the completely unpolarized light and in order to test that the code adheres to the stated orientation conventions. Having appended these two initially polarized cases to our tables, it also allows for interpolation of reflection results for any input polarization state from this basis of the three computed independent polarization states, which will be necessary in future construction of global spectropolarimetric models. We aim to address any impact of light bending and other relativistic effects for a distant observer in our future works that will introduce the stokes local tables integrated over the accretion disc with some adopted global geometry.

2021MNRAS.501.3781R__Nisini_et_al._2005_Instance_1

While spatially extended optical jets and bipolar CO molecular outflows have been observed in numerous Class 0/I protostars (e.g. Reipurth & Bally 2001; Bally 2016, and references therein), near-infrared high-resolution spectroscopy and spectroimaging observations in the past two decades have made it possible to study the kinematics of the outflowing gas and physical properties at the base of the jet within a few hundred au of the driving source in Class 0/I protostars (e.g. Davis et al. 2001, 2003, 2011; Nisini et al. 2005, 2016; Caratti o Garatti et al. 2006; Takami et al. 2006; Antoniucci et al. 2008, 2011, 2017; Garcia Lopez et al. 2008, 2013). These microjets are bright in [Fe ii] forbidden and H2 rovibrational emission lines, hence showing the presence of forbidden emission-line (FEL) regions and molecular hydrogen emission-line (MHEL) regions in low-mass Class 0/I protostars. While multiple low- and high-velocity components are observed in both MHELs and FELs, the higher velocity gas is slightly further offset from the driving source than the slower gas, and the kinematics of the H2 emission differs from [Fe ii] emission, revealing complicated kinematic structures. Evidence of H2 emission from cavity walls is also seen in some protostars, suggesting the presence of a wide-angled wind. Strong emission in the well-known accretion diagnostics of Paschen and Brackett hydrogen recombination lines is observed in protostars, with the ratio of the accretion luminosity to bolometric luminosity spanning from ∼0.1 to ∼1. The mass accretion and loss rates for Class 0/I low-mass protostars span the range of 10−6–10−8 M⊙ yr−1, and the derived jet efficiencies (ratio between mass ejection and accretion rates) range between ∼1 per cent and 10 per cent (e.g. Davis et al. 2001, 2003, 2011; Nisini et al. 2005, 2016; Caratti o Garatti et al. 2006; Takami et al. 2006; Antoniucci et al. 2008, 2011, 2017; Garcia Lopez et al. 2008, 2013). These measurements are within the range predicted by the magnetohydrodynamic jet launching models (e.g. Frank et al. 2014).

2018ApJ...866L...1S__Pecharromán_et_al._1999_Instance_7

It was found that the complex dielectric function from Pecharromán et al. (1999) for the sample obtained by heating bayerite at 1273 K, assuming a spheroid with depolarization parameters of (0.35, 0.003), produced an opacity with 11, 20, 28, and 32 μm features, so this component was included in the models. However, with only this component, the observed 20 μm features in the residual spectra were found to be wider than those in the models. By adding the opacity of the sample obtained by heating boehmite at 1173 K, the width of the 20 μm feature could be matched. This was done using the complex dielectric function for the sample obtained by heating boehmite at 1173 K from Pecharromán et al. (1999), assuming a spheroid with depolarization parameters of (0.35, 0.035). The complex dielectric functions of the samples obtained by heating bayerite and boehmite to various temperatures (Pecharromán et al. 1999) were derived by modeling the reflectance spectra of pellets obtained by pressing powders of these materials under great pressure. This method required Pecharromán et al. (1999) to assume an effective medium theory, such that a pellet is a mixture of one of their samples with a matrix of air. Pecharromán et al. (1999) noted that heating bayerite at 500°C eliminates the XRD pattern of bayerite, and they note that at 700°C, the infrared reflectance spectrum of the boehmite sample no longer shows OH− stretching bands. This must mean that the samples obtained from heating bayerite at 1273 K and from heating boehmite at 1173 K are no longer bayerite or boehmite, respectively. XRD performed by Pecharromán et al. (1999) of the sample of bayerite prepared at 1273 K suggests only θ-alumina was present, and their infrared and NMR spectroscopy confirms this. XRD of their sample obtained from heating boehmite to 1173 K (Pecharromán et al. 1999) suggests δ-alumina to be present, though some amounts of θ-alumina and α-alumina are present, as they deduce from XRD and infrared and NMR spectroscopy.

2019MNRAS.487.1210T__McNamara_&_Nulsen_2007_Instance_2

On larger scales, the clusters in which BCGs reside can generally be divided into two categories: cool core clusters, which exhibit very peaked surface brightness distributions at X-ray wavelengths, and non cool core clusters, with similar overall X-ray luminosities but with smoother, less peaked X-ray surface brightness distributions. Some authors (e.g. Hudson et al. 2010; Santos et al. 2010) define an intermediate category called moderate or weak cool core clusters. Since cool core clusters have short radiative cooling time-scales on the order of 108 yr in their centres (e.g. Voigt & Fabian 2004; McNamara & Nulsen 2007, 2012; Hlavacek-Larrondo et al. 2012), starbursts are expected to be common at the centre of such clusters. Indeed, the central cool gas in these clusters should condense onto the BCG, forming stars at rates of hundreds of solar masses per year (e.g. Fabian 1994). However, most BCGs are relatively quiescent and those that do show evidence of star formation generally tend to have star formation rates 1 order of magnitude smaller, on the order of $1-150 \, \mathrm{M_{\odot }\, {yr}^{-1}}$ (e.g. Donahue et al. 2007; Bildfell et al. 2008; O’Dea et al. 2008, 2010; Rawle et al. 2012). This mismatch between expected and observed star-forming rates, known as the cooling flow problem, is thought to be caused by active galactic nuclei (AGNs) feedback processes from the BCG. AGNs can release copious amounts of energy into the intracluster medium (ICM) through many ways, including: jetted outflows that inflate cavities, weak shocks, sound waves, or turbulence in the ICM (e.g. Markevitch & Vikhlinin 2007; McNamara & Nulsen 2007, 2012; Zhuravleva et al. 2014; Fabian et al. 2017). Alone, the energy released by jetted outflows appears to be on the same order as the energy needed to offset cooling (e.g. Rafferty et al. 2006; McNamara & Nulsen 2007; Hlavacek-Larrondo et al. 2012), therefore suggesting that AGN feedback is a good candidate for solving the cooling flow problem.

2021MNRAS.506.5935R__Ellison_et_al._2011_Instance_1

The discovery of a correlation between the mass of supermassive black holes (SMBHs) and several properties of their host galaxies (e.g. Magorrian et al. 1998; Ferrarese & Merritt 2000; Gebhardt et al. 2000; Kormendy & Ho 2013) has suggested that the growth of SMBHs and their host galaxies are tightly connected. Mergers of galaxies are thought to be one of the most important mechanisms with which galaxies build up their stellar masses (White & Rees 1978). Both observational (e.g. Lonsdale, Persson & Matthews 1984; Joseph & Wright 1985; Armus, Heckman & Miley 1987; Clements et al. 1996; Alonso-Herrero et al. 2000; Ellison et al. 2008) and theoretical (e.g. Mihos & Hernquist 1996; Di Matteo et al. 2007) studies have shown that galaxy mergers enhance star formation (SF). Simulations have also shown that the interaction between two or more galaxies can reduce the angular momentum of the circumnuclear material (e.g. Barnes & Hernquist 1991; Blumenthal & Barnes 2018), thus providing an effective mechanism to trigger accretion on to SMBHs (e.g. Di Matteo, Springel & Hernquist 2005). Observationally, several works have confirmed this scenario. Koss et al. (2010) and Silverman et al. (2011) found a higher AGN fraction in pairs than in isolated galaxies with similar stellar masses. It has been shown that the fraction of AGN in mergers tends to increase as the separation between the two galaxies decreases (Ellison et al. 2011), and peaks after coalescence (Ellison et al. 2013). Koss et al. (2012) have shown that the average luminosity of dual AGN also increases with decreasing separation (see also Hou, Li & Liu 2020), and it is higher for the primary (i.e. more massive) component of the system (see also De Rosa et al. 2019, for a recent review). While AGNs with moderate X-ray luminosities are typically found in non-interacting disc galaxies (e.g. Koss et al. 2011; Kocevski et al. 2012; Schawinski et al. 2012), more luminous objects are commonly found in merging systems (e.g. Treister et al. 2012; Glikman et al. 2015; Hong et al. 2015). Treister et al. (2012) showed that, while for 2–10 keV AGN luminosities of $L_{2-10}\sim 10^{41}\rm \, erg\, s^{-1}$ only a small fraction (${\lt}1{{\ \rm per\ cent}}$) of AGNs are in mergers, at $L_{2-10}\sim 10^{46}\rm \, erg\, s^{-1}$ ∼ 70–80 per cent of the sources are found in interacting systems (see also Glikman et al. 2015). Recent evidence has suggested that hot dust obscured galaxies (Hot DOGs; Wu et al. 2012; Assef et al. 2015), which are some of the most luminous galaxies observed so far ($L_{\rm \, IR} \gt 10^{13} \mathrm{ L}_{\odot }$), are also found in mergers (e.g. Fan et al. 2016). These observations suggest that, while at low luminosities SMBH accretion is triggered by secular processes, at high luminosities mergers can play a dominant role. This is in agreement with the evolutionary scenario proposed by Sanders et al. (1988) for ultra-luminous [$L_{\rm \, IR}(8\!-\!1000\, \mu \rm m)\ge 10^{12}\,L_{\odot }$] infrared galaxies (ULIRGs; e.g. Sanders & Mirabel 1996; Pérez-Torres et al. 2021). In this scheme, two gas-rich disc galaxies collide, triggering SF and accretion on to the SMBH. The strong accretion on to the SMBH would lead the source to evolve first in a luminous red quasar (e.g. Urrutia, Lacy & Becker 2008; Glikman et al. 2015; LaMassa et al. 2016) and then in an unobscured blue quasar.

2021ApJ...911...79P___2007_Instance_1

Now we turn to the question of whether there are any general trends or strong correlations between the degree of HSP compliance, the average Fidx, and the average Φ derived from the AR samples contained in the defined HRs. As shown in the scatter plot of the average Fidx versus the HSP (Figure 5(a)), we find a weak tendency that HRs with lower degrees of HSP compliance show larger values of the average Fidx, with the linear Pearson correlation coefficient (PCC) of −0.55. In the scatter plot, the anti-HSP region with the largest average Fidx can be considered as an extreme case, which lies far away from both the vertical and horizontal dashes lines used to separate data points of the five lowest HSP and the five largest average Fidx, respectively. On the other hand, a positive correlation exists between the average Φ and the average Fidx with the linear PCC = 0.65 (refer to Figure 5(b)). Such correlation of Φ with flaring activity in individual ARs has been reported in many previous studies (e.g., Leka & Barnes 2003, 2007; Park et al. 2010; Liu et al. 2017; Lee et al. 2018), but it is reported here for the first time on this larger spatial scale of the HRs over a much longer period of solar cycle 24. As shown in Figure 5(c), a negative, although weak, correlation appears between the HSP and the average Φ with the linear PCC = −0.36. In contrast, a weak trend was reported in Paper I that ARs with larger values of Φ show a higher HSP. These two contrasting trends of the HSP with the average Φ are mainly due to the different ways of dividing the same AR samples into smaller subsets. The AR subsets in this study were selected based on heliographic locations of the AR samples in the defined Carrington longitude–latitude plane, while those in Paper I as a function of Φ values. The two highest flare-productive HRs with the average Fidx ≥15 have the average Φ values ranked in the top two as well as lower degrees of HSP compliance (i.e., one with the lowest HSP and the other with the HSP in the bottom 20%). Meanwhile, the two highly flare-productive regions can be considered as obvious outliers, compared to the positive trend of the HSP with respect to the average Φ in Paper I. This may indicate that the HSP for those regions is obscured by vigorous turbulent convective flows interacting with rising flux tubes therein. As mentioned earlier, the two X-class flaring ARs, NOAA 12673 and NOAA 12192, are located at the two HRs, respectively. Even excluding these two influential ARs, however, all of the trends as described in Figure 5 remain the same, although the correlations become less strong (i.e., the linear PCCs of −0.25, 0.51, and −0.27 for the cases in Figures 5(a), (b), and (c)).

2016MNRAS.463.3783B__Reid_&_White_2011_Instance_2

It is well known that a per cent level understanding of the anisotropy of the redshift-space galaxy clustering is needed to accurately recover cosmological information from the RSD signal in order to shed light on the issue of dark energy versus modified gravity. From a statistical point of view, the source of the anisotropy is the galaxy line-of-sight pairwise velocity distribution. It is therefore important to adopt a realistic functional form for this velocity PDF when fitting models to the data. To this purpose, in Paper I we introduced the GG prescription for the velocity PDF. In this work, we have continued the development of this model by making explicit the dependence of the GG distribution on quantities predictable by theory, namely its first three moments, and extending it to the more general concept of GQG. To keep the model as simple as possible, we have proposed an ansatz with two free dimensionless parameters that describe how infall velocity and velocity dispersion vary when moving from one place to another in our Universe. Since their interpretation is clear, these parameters can be theoretically predicted or, assuming a more pragmatic approach, tuned to simulations or used as nuisance parameters. State-of-the-art PT has proven successful in predicting the large-scale behaviour of the velocity PDF and the correspondent monopole and quadrupole of the redshift-space correlation function (e.g. Reid & White 2011; Wang, Reid & White 2014), at least for massive haloes, M ∼ 1013 M⊙. Unfortunately, by definition, any PT breaks down for small separations. Consequently, alternative approaches have been suggested in the literature, spanning from purely theoretical (e.g. Sheth 1996) to hybrid techniques in which N-body simulations plus an HOD are employed to deal with the issue of non-linearities (e.g. Tinker 2007; Reid et al. 2014). One of the main results from our work is to provide a framework in which perturbation and small-scale theories are smoothly joined, so that all available RSD information can be coherently extracted from redshift surveys. A fundamental requirement for a redshift-space model is that it must be precise on all scales interest, and it should inform the user of the scales on which the model can be trusted. We have compared to N-body simulations the well-known GSM (Reid & White 2011), the more recent ESM (Uhlemann et al. 2015) and the GQG prescription over a broad range of separations, from 0 to 80 h−1 Mpc. Different redshifts, from z = 0 to 1, and different tracers, namely DM particles and two mass-selected catalogues of DM haloes, have been considered. We have concluded that, among the three, QGQ is the only model capable of providing a precise redshift-space correlation function on scales down to ∼5 h−1 Mpc over the range of redshifts covered by future surveys. Keeping in mind that the range of validity of the models depends on tracer, redshift and order of the Legendre multipoles we are interested in, for finiteness, we can say that all the models converge to the expected amplitude on scales ≳30 h−1 Mpc, at least for multipole and quadrupole. Since these scales roughly coincide with the range of validity of state-of-the-art PTs, if we rely only on PT and if we are not interested in higher order multipoles, the most natural choice is the simplest model among the three, i.e. the GSM. As for the ESM, we have found it to be unbiased down to smaller scales and for higher order multipoles than the GSM, thus confirming the results by Uhlemann et al. (2015), but, on the other hand, it seems to behave even worse than the GSM on the smallest scales. We can therefore think of it as a natural extension of the GSM in the perspective of further PT developments. In particular, a better prediction of the third moment of the velocity PDF is required before the ESM can be applied to data on smaller scales. Formally, the same argument holds for the GQG model, none the less, since this latter is meant to include non-linear scales, it could be possible to obtain a prediction for the third moment by interpolating between (very) small and (very) large scales. More precisely, as shown in the lower-right panel of Fig. A1, the functions $c^{(3)}_t$ and $c^{(3)}_r$, which fully characterize the third moment, are peaked at r ≲ 10 h−1 Mpc. By adopting a model for the small-scale limit that includes those separation, most likely using simulations in a similar way to that proposed in Reid et al. (2014), we would then be able to interpolate between these peaks and their large-scale limit, which is trivially 0.

2018ApJ...856..140H__Leary_et_al._2009_Instance_1

The number of BHs, their mass distribution, and the number density are poorly known in NSCs. Theoretically, a single-mass distribution of objects forms a power-law density cusp around a massive object with n(r) ∝ r−1.75 (Bahcall & Wolf 1976), where n(r) is the number density, and r is the distance from the MBH. For multi-mass distributions, lighter and heavier objects develop shallower (∝r−1.5) and steeper cusps (typically ∝r−2 to r−2.2, and r−3 in extreme cases), respectively (Bahcall & Wolf 1977; Freitag et al. 2006; Hopman & Alexander 2006b; Keshet et al. 2009; Aharon & Perets 2016). Recent observations of the stellar distribution in the Milky Way NSC identify a cusp with n(r) ∝ r−1.25 (Gallego-Cano et al. 2018; Schödel et al. 2018), which is consistent with the profile after a Hubble time (Baumgardt et al. 2018). As BHs are heavier than typical stars, they are expected to relax into the steeper cusps. The relaxation time of BH populations is much shorter: 0.1–1 Gyr (O’Leary et al. 2009), although it can become much longer than that in the case of a shallow stellar density profile (Dosopoulou & Antonini 2017). In this work, we assume that the BH number density follows a cusp with either n(r) ∝ r−2 or r−3 in our two sets of calculations.The BH mass in the two cases is set arbitrarily to 107 and 4 × 106 M⊙, respectively, and we refer to the two models as Bahcall–Wolf–like (BW) and GC examples. Note that here GC refers to the assumed MBH mass (Ghez et al. 2008) and the observed stellar distribution (see below). In reality, the cusp distribution varies with the BH mass. Thus, we have also generated initial conditions for the BW case so that β in the number density distribution, n(r) ∝ r−(3/2+β), is calculated by β(m) = m/4M0, where m is the binary mass, and M0 is the weight average mass (e.g., Alexander & Hopman 2009; Keshet et al. 2009; Aharon & Perets 2016). We found that, due to the stability conditions, the initial condition distribution does not change significantly. For the GC case, the initial conditions’ distribution will change more significantly if we allow β to vary with the mass. However, we keep β = 3 to investigate the effects of a steep number density distribution on the rates. As we will show later in this work, the choice of the number density distribution will have a very limited effect on the merger rate.

2017ApJ...845...86E__Soler_&_Terradas_2015_Instance_2

Among the suggestedmechanisms responsible for the strong damping of the coronal loop oscillations (e.g., Ruderman & Roberts 2002; Ofman 2005, 2009; Morton & Erdélyi 2009) resonant absorption of the MHD waves, which was established first by Ionson (1978), is a strong candidate. Several works developed this theory (e.g., Davila 1987; Sakurai et al. 1991a, 1991b; Goossens et al. 1995; Goossens & Ruderman 1995; Erdélyi 1997; Cally & Andries 2010). The necessary condition for the resonant absorption is a continuum of Alfvén or slow frequency across the loop (Ionson 1978; Hollweg 1984, 1987; Davila 1987; Sakurai et al. 1991a). Resonant absorption occurs when the frequency of the global MHD mode matches at least with one of the frequencies of the background Alfvén or slow continuum at a location called he resonance point. As a result, the energy of the global MHD mode transfers to the local Alfvén modes in a layer around the resonance point, named the resonance layer (Lee & Roberts 1986; see also Goossens et al. 2013; Soler & Terradas 2015). In the absence of dissipation mechanisms, the amplitude of the oscillations diverges at the resonance point. Dissipation is important in the resonance layer, where the oscillations make large gradients. The background Alfvén or slow continuum can be due to the variation of the plasma density (e.g., Davila 1987; Ofman et al. 1994; Ruderman & Roberts 2002; Terradas et al. 2006; Soler & Terradas 2015), twisted magnetic field (Ebrahimi & Karami 2016), or both of them together (Karami & Bahari 2010; Giagkiozis et al. 2016). There are a variety of theoretical works related to the damping of the coronal loop oscillations based on the theory of resonant absorption of MHD waves (e.g., Goossens et al. 2002, 2009; Ruderman & Roberts 2002; Van Doorsselaere et al. 2004; Andries et al. 2005; Terradas et al. 2006; Karami et al. 2009; Karami & Bahari 2010; Soler et al. 2013; Soler & Terradas 2015; Ebrahimi & Karami 2016; Giagkiozis et al. 2016; Jung Yu & Van Doorsselaere 2016). For a good review about the theory of resonant absorption, see also Goossens et al. (2011).

2017ApJ...850L..40A__Yang_et_al._2017_Instance_1

Aided by the tight localization constraints of the three-detector network and the proximity of the GW source, multiple independent surveys across the EM spectrum were launched in search of a counterpart beyond the sGRB (Abbott et al. 2017c). Such a counterpart, SSS17a (later IAU-designated AT 2017gfo), was first discovered in the optical less than 11 hours after merger, associated with the galaxy NGC 4993 (Coulter et al. 2017a, 2017b), a nearby early-type E/S0 galaxy (Lauberts 1982). Five other teams made independent detections of the same optical transient and host galaxy all within about one hour and reported their results within about five hours of one another (Allam et al. 2017; Arcavi et al. 2017a, 2017b; Lipunov 2017b; Tanvir & Levan 2017; Yang et al. 2017; Soares-Santos et al. 2017; Lipunov et al. 2017a). The same source was followed up and consistently localized at other wavelengths (e.g., Corsi et al. 2017; Deller et al. 2017a, 2017b, 2017c; Goldstein et al. 2017; Haggard et al. 2017a, 2017b; Mooley et al. 2017; Savchenko et al. 2017; Alexander et al. 2017; Haggard et al. 2017c; Goldstein et al. 2017; Savchenko et al. 2017). The source was reported to be offset from the center of the galaxy by a projected distance of about 10″ (e.g., Coulter et al. 2017a, 2017b; Haggard et al. 2017a, 2017b; Kasliwal et al. 2017; Yang et al. 2017; Yu et al. 2017). NGC 4993 has a Tully–Fisher distance of ∼40 Mpc (Freedman et al. 2001; NASA/IPAC Extragalactic Database164 164 The NASA/IPAC Extragalactic Database (NED) is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. ), which is consistent with the luminosity distance measurement from gravitational waves ( Mpc). Using the Tully–Fisher distance, the ∼10″ offset corresponds to a physical offset of ≃2.0 kpc. This value is consistent with offset measurements of sGRBs in other galaxies, though below the median value of ∼3–4 kpc (Fong et al. 2010; Fong & Berger 2013; Berger 2014).

2018ApJ...856..136P__Burkhart_et_al._2010_Instance_1

Depending on the specific driver, the characteristics of turbulence will then be imprinted within the ISM mainly as three-dimensional density and velocity fluctuations, and these fluctuations have been traditionally studied via correlation functions such as the spatial power spectrum (SPS) (e.g., Crovisier & Dickey 1983), Δ-variance (e.g., Stutzki et al. 1998), and structure function (e.g., Padoan et al. 2002; Burkhart et al. 2015b). In particular, the SPS approach has been applied to observations of various Galactic and extragalactic environments (e.g., Plume et al. 2000; Dickey et al. 2001; Elmegreen et al. 2001; Burkhart et al. 2010; Combes et al. 2012; Zhang et al. 2012; Pingel et al. 2013), showing power spectral slopes β roughly ranging from −2.7 to −3.7 depending on the tracers used (e.g., H i, carbon monoxide (CO), and dust). These slopes essentially provide information on the relative amount of structure as a function of spatial scale and can be compared with theoretical models of turbulence (mainly numerical simulations) to characterize turbulence cascade (e.g., Burkhart et al. 2010), to determine the influence of shocks (e.g., Beresnyak et al. 2005), to reveal the injection and dissipation scales of turbulent energy (e.g., Kowal & Lazarian 2007; Federrath & Klessen 2013; Chen et al. 2015), and to trace the evolution of MCs (e.g., Burkhart et al. 2015a). The proximity and abundance of multi-wavelength observations make MCs in the solar neighborhood an ideal laboratory for probing the impact of turbulence on their formation and evolution. In this paper, we focus on the Perseus MC, which is a nearby (∼300 pc; e.g., Herbig & Jones 1983; Černis 1990), low-mass (∼2 × 104 M⊙; e.g., Sancisi et al. 1974; Lada et al. 2010) cloud. Its star formation activities, as well as atomic and molecular gas content, have been extensively examined over the past decade (e.g., Ridge et al. 2006; Jørgensen et al. 2007; Pineda et al. 2008; Lee et al. 2012, 2014, 2015; Mercimek et al. 2017), revealing that the cloud consists of several individual dark and star-forming regions (e.g., B5, B1, B1E, IC 348, and NGC 1333) and is actively forming low- to intermediate-mass stars (see Bally et al. 2008 for a review).

2015ApJ...806...15Z___2015_Instance_1

Since the launch of Swift (Gehrels et al. 2004) in 2004, the nature of jet breaks in gamma-ray burst (GRB) afterglows has become increasingly puzzling. There is strong evidence that the ejecta from the GRB central engine must be jet-like (Zhang & Mészáros 2004). Thus a collimation correction factor, , where is the jet opening angle with typical values of 5°–10°, can be applied to relieve the energy budget problem, so that a typical GRB energy is erg, where is the isotropic equivalent energy release in gamma-rays. Such collimated ejecta expand outward relativistically with Lorentz factors Γ of several hundred initially. Internally, the ejecta release their energy through internal shocks (Rees & Meszaros 1994; Kobayashi et al. 1997; Daigne & Mochkovitch 1998), or magnetic dissipation processes (e.g., the ICMART model; Zhang & Yan 2011) or photospheric dissipation (e.g., Guiriec et al. 2010, 2011, 2015; Lazzati & Begelman 2010; Ryde et al. 2010, 2011; Pe’er & Ryde 2011) and produce the prompt gamma-ray emission of GRBs. Externally, the ejecta are further decelerated by an ambient medium (e.g., a constant density interstellar medium (ISM); or a stellar-wind environment with density inversely proportional to distance squared) and produce long-term broadband afterglows through external shocks (see e.g., Gao et al. 2013, for a review). Due to relativistic beaming, only a portion of the radiation from the ejecta front surface, which is within a cone of half-opening angle , can be observed (Rhoads 1997, for reviews see Piran 2004; Granot 2007; van Eerten 2013). An unavoidable consequence of this general picture is that when the ejecta are decelerated to , the light curve should steepen because (1) the maximum observable portion of the ejecta (the cone of the whole jet with opening angle ) is now smaller than that which is expected (a cone with half-opening angle and (2) the onset of lateral spreading of the ejecta, predicted to become noticeable in the observer frame around the same time (Rhoads 1999), causes the blast wave to decelerate further. Such a “jet break” in a GRB light curve is expected to behave achromatically because it only reflects the ejecta geometry, under the assumption that the afterglow emission regions and mechanisms do not change in different spectral regimes (Rhoads 1999; Sari 1999; Huang et al. 2000; Granot et al. 2002; see also reviews by Mészáros 2002; Piran 2004; Zhang & Mészáros 2004). The achromaticity was apparently confirmed in the optical and near-IR band in a few cases of pre-Swift GRBs (Kulkarni et al. 1999; Harrison et al. 2001; Klose et al. 2004).

2018ApJ...864..160R__Bhat_et_al._2013_Instance_1

Figure 9 shows the components required for such a time-domain survey, with the PC beamformer as specified above. The required components are shown in four different colors. Visibilities computed in the uGMRT backend (GWB; marked in blue) at 1 ms time resolution are transferred to the the PC beamformer nodes (marked in orange) with an aggregate data rate of ∼3 GB s−1. We aim to implement in-field phasing (Kudale & Chengalur 2017) using a sky model derived from the time-averaged visibilities in order to improve the coherence in phasing up to a baseline length of several kilometers. This optimizes the GMRT PA sensitivity beyond a central compact core (most current PA observations use only the antennas in the central square). In addition to deriving the phasing model, a baseline-based flag masking the bad baselines will also be generated in real time from these time-averaged visibilities. Coherent additions of these visibilities will result in 128 such visibility beams. The multi-DM search for single pulses (colored in yellow) on each of these visibility beams would need to be executed on a separate FRB cluster, followed by coincidence filtering to remove spurious events (Bhat et al. 2013). We also propose recording these 128 beams with a 1 ms time resolution, giving a total data rate of 200 MB s−1 into a disk for a quasi-real-time search for pulsars using the same cluster. We note that the proposed 1 ms time resolution is sufficient to detect double neutron star systems, young pulsars, and normal pulsars, as well as objects like radio magnetars. Visibility buffers corresponding to candidate single-pulse events will be recorded at a 1 ms time resolution covering the full DM sweep time-range. For an event at a DM of 2000 pc cm−3, the total DM sweep time over 200 MHz band in uGMRT 300–500 MHz band is ∼50 s, which results in a 40 GB buffer size on each of the PC beam nodes. This means one can easily hold few buffers for accommodating the pipeline delay and flush them into storage based on the real-time triggers. These visibilities will be processed through the processing blocks (marked in green) for millisecond imaging localization at quasi-real-time. This block includes removal of dispersion delay, followed by a flagging and calibration pipeline and snapshot imaging. We note that part of this imaging pipeline for localizing time-domain events has already been demonstrated for the GHRSS Phase1 survey (Bhattacharyya et al. 2016).

2017AandA...601A..72I__Kobayashi_&_Tanaka_2010_Instance_3

Small grains, which contribute most to infrared emission, are removed by collisional fragmentation and blown out by radiation pressure. The removal timescale is much shorter than the ages of host stars. Disruptive collisions among underlying large bodies, which are called planetesimals, produce smaller bodies and collisional fragmentation among them results in even smaller bodies. This collisional cascade continues to supply small grains. The evolution of debris disks has been explained by the steady-state collisional cascade model (e.g., Wyatt 2008; Kobayashi & Tanaka 2010): the total mass of bodies decreases inversely proportional to time t. Therefore, the excess ratio (Fdisk/F∗) is given by (2)\begin{equation} \frac{F_{\rm disk}}{F_{*}} = \frac{t_0}{t},\label{cc} \end{equation}FdiskF∗=t0t,where t0 is the dissipation timescale that is determined by the collisional cascade. Under the assumption of the steady state of collisional cascade, the power-law size distribution of bodies is analytically obtained and the power-law index depends on the size dependence of the collisional strength of bodies (see Eq. (32) of Kobayashi & Tanaka 2010). In the obtained size distribution, erosive collisions are more important than catastrophic collisions (see Fig. 10 of Kobayashi & Tanaka 2010). Taking into account the size distribution and erosive collisions, we derive t0 according to the collisional cascade (see Appendix E for derivation), (3)\begin{eqnarray} t_0&amp;\sim&amp; 1.3 \left( \frac{s_{\rm p}}{\rm 3000\,km} \right)^{0.96} \left( \frac{R}{\rm 2.5\,au} \right)^{4.18}\nonumber\\ &amp;&amp;\quad\times \left(\frac{\Delta R}{0.4 R}\right) \left( \frac{e}{\rm 0.1} \right)^{-1.4} {\rm Gyr},\label{eq:t0} \end{eqnarray}t0~1.3sp3000 km0.96R2.5 au4.18where sp is the size of planetesimals, R is the radius of the planetesimal belt, and e is the eccentricity of planetesimals. Interestingly, t0 is independent of the initial number density of planetesimals (Wyatt et al. 2007). Note that the perturbation from Moon-sized or larger bodies is needed to induce the collisional fragmentation of planetesimals (Kobayasi & Löhne 2014), which is implicitly assumed in this model.

2020ApJ...897...94G__Stanway_&_Eldridge_2019_Instance_1

It is interesting that the sources with suspected or confirmed Lyman continuum radiation at high redshifts are peculiar and rare, bright SFGs with rather hard ionizing spectra marked by high-ionization emission lines (e.g., N v, C iv, He ii, O iii, C iii). Their presence at high redshift can be hardly explained by pure SFGs, even requiring uncommon assumptions (e.g., large stellar rotation, binary stellar population, top-heavy initial mass function, extremely low metallicity) as discussed by a number of recent works (see Bowler et al. 2014; Bradley et al. 2014; Kehrig et al. 2015; Stark et al. 2015; Jaskot & Ravindranath 2016; Stark 2016; Senchyna et al. 2017, 2019, 2020; Berg et al. 2018; Nakajima et al. 2018; Chisholm et al. 2019; Jaskot et al. 2019; Le Fevre et al. 2019b; Nanayakkara et al. 2019; Schaerer et al. 2019; Stanway & Eldridge 2019). The majority of galaxies showing Lyman continuum emission (both at low z and at z ∼ 3–4) populate the upper end of the Baldwin, Phillips, and Terlevich (BPT) diagram (Baldwin et al. 1981) or occupy a region of high-ionization line ratio in between SFGs and AGNs, or mainly populated by AGNs (see, e.g., Figures 11 and 14 by Nakajima et al. 2018). Indeed, Le Fevre et al. (2019b) find a marginal 2σ detection in the X-ray stacking of strong C iii emitters at 2 z 4, consistent with the presence of low-luminosity AGNs. Interestingly, evidence recently emerged that local confirmed Lyman continuum emitters or low-z Green Peas, blue compact galaxies, Lyman break analogs, which are usually associated with reliable Lyman continuum candidates, are characterized by significant X-ray emission, not compatible with star formation activity but more plausibly powered by low-luminosity AGN activity or by a large population of high-mass X-ray binaries (Kaaret et al. 2017; Bao et al. 2019; Bluem et al. 2019; Latimer et al. 2019; Plat et al. 2019; Prescott & Sanderson 2019; Senchyna et al. 2019, 2020; Svoboda et al. 2019; Wu et al. 2019; Baldassare et al. 2020; Birchall et al. 2020; Dittenber et al. 2020). It is thus possible that pure stellar radiation from SFGs is a negligible source of H i ionizing radiation, and the bulk of Lyman continuum photons escaping at low and high z are produced instead by accretion onto supermassive black holes (SMBHs).

2017AandA...599A..55B__Shakura_1972_Instance_1

When the characteristic time of variability of the mass flux along the accretion disk is longer than the relaxation time of the local disk equilibrium, it is possible to use the approximation of local equilibrium Shakura (1972), see also Bisnovatyi-Kogan (2011), to calculate the transient disk structure. The equilibrium along a radius of the accretion disk around a star with a mass M is determined by the Keplerian rotational velocity ΩK(1)\begin{equation} \Omega=\Omega_{\rm K}=\left(\frac{GM}{r^3}\right)^{1/2}\cdot \label{omega} \end{equation}Ω=ΩK=GMr31/2·Writing the equation of the vertical equilibrium in approximate algebraic form, we obtain (2)\begin{equation} h=\sqrt 2 \frac{v_{\rm s}}{\Omega}, \label{h} \end{equation}h=2vsΩ,where \hbox{$v_{\rm s}=\sqrt{P/\rho}$}vs=P/ρ is the speed proportional to the sound velocity, P and ρ are the (gas + radiation) pressure and density at the symmetry plane of the accretion disk, and h is the semi-thickness of the accretion disk. The specific angular momentum l of the matter in the accretion disk is connected to the rotation velocity as(3)\begin{equation} l=r\,v_\phi=r^2\Omega. \label{l} \end{equation}l=r vφ=r2Ω.The mass flux through the disk at radius r is connected to the radial velocity vr as (4)\begin{equation} \dot M=-4\pi h\rho r v_r, \quad \dot M>0,\quad v_r<0. \label{mflux} \end{equation}Ṁ=−4πhρrvr, Ṁ>0, vr0.We use an α approximation for the turbulent viscosity (Shakura 1972) when the (rφ) component of the stress tensor trφ is written as (5)\begin{equation} t_{r\phi}=\alpha\, P, \label{trphi} \end{equation}trφ=α P,where the phenomenological non-dimensional parameter α ≤ 1. The condition of stationarity of the angular momentum, in which the outward viscous radial flux of the angular momentum is balanced by the angular momentum of the inward flux of the mass, is written as (see, e.g., Bisnovatyi-Kogan 2011) (6)\begin{equation} r^2 h \alpha P=\frac{\dot M}{4\pi}(l-l_{\rm in}). \label{angmom} \end{equation}r2hαP=Ṁ4π(l−lin).The main input into the time lag comes from the outer regions of the disk with l ≫ lin. Then we have from Eqs. (4) and (6) the expression for the radial velocity in the form (7)\begin{equation} v_r=-\alpha\frac{v_{\rm s}^2}{v_\phi}\cdot \label{vr} \end{equation}vr=−αvs2vφ·We also define the surface density Σ, and write Eq. (6) in light of Eq. (3), using condition l ≫ lin, in the form (8)\begin{equation} \Sigma=2\rho h,\quad \dot M \Omega=4\pi \alpha P h. \label{sigma} \end{equation}Σ=2ρh, ṀΩ=4παPh.The equation of the local thermal balance in the accretion disk, when the heat produced by viscosity Q+ is entirely emitted through the sites of the optically thick accretion disk with a total flux Q−, at l ≫ lin is written as (see, e.g., Bisnovatyi-Kogan 2011)(9)\begin{equation} \frac{3}{2}\dot M \Omega^2=\frac{16\pi ac T^4}{3\varkappa \Sigma}\cdot \label{theq} \end{equation}32ṀΩ2=16πacT43ϰΣ·Here T is the temperature in the symmetry plane of the accretion disk, a is the constant of the radiation energy density, c is the speed of light, and ϰ is the Thompson (scattering) opacity of the matter.

2019MNRAS.482..194B__Choi_et_al._2015_Instance_1

Observationally, it is a big challenge to conclude whether AGN’s feedback could regulate star formation or not, and how it regulates star formation. On one hand, if strong outflows emerge, they could clear out the star-forming gas to suppress star formation (Alexander & Hickox 2012; García-Burillo et al. 2014; Alatalo et al. 2015; Hopkins et al. 2016; Wylezalek & Zakamska 2016). The heating by jets propagating through the galaxies could prevent gas from cooling and cut-off the gas supply for further star formation (Karouzos et al. 2014; Choi et al. 2015). On the other hand, the outflows and jets interact with the gas in host galaxies and compress it to trigger new star formation (Silk 2013; Zubovas et al. 2013; Zubovas & Bourne 2017). In fact observations show either no or positive relationships between star formation rates and SMBH accretion rates but no negative trends are seen (Shi et al. 2007, 2009; Baum et al. 2010; Xu et al. 2015; Zhang et al. 2016; Mallmann et al. 2018). Whether AGN’s feedback plays the role may also depends on the spatial scale that observations could resolve and time-scale that the observed tracers could probe (Harrison et al. 2012; Cresci et al. 2015; Feruglio et al. 2015). For example, radiation from AGNs nearly instantaneously impact the surrounding ISM while the attenuation from ISM probably limits their impact to the nuclear regions (Roos et al. 2015). Outflows or jets travel slowly and may be decelerated after interactions with ambient gas, which delays their effects on star formation at large distances from the nuclei (Harrison 2017; Harrison et al. 2018). Feedback by jets or outflows on ISM also depends on their orientation relative to the dusty torus, making their effects on star formation to be anisotropic. The short duty cycle of AGNs could also make feedback by radiation from AGNs temporally variable in strength. Case studies of individual AGNs find evidence of coexistence of positive and negative feedback on star formation (Zinn et al. 2013), suggesting the complicated nature of AGN feedback.

2019MNRAS.490.1870L__Guo_et_al._2015_Instance_2

In principle, we could measure the multipole moments in a given simulation by directly populating dark matter haloes in the simulation with galaxies. However, this is computationally very expensive and comes with realization noise due to the random number and phase-space positions of galaxies. Instead, we use a tabulation method (Neistein & Khochfar 2012; Reid et al. 2014; Zheng & Guo 2016) to speed up the computation dramatically and eliminate any realization noise. We first take all haloes in a given simulation to serve as tracers of central galaxies. We furthermore assign to each halo of mass M a Poisson number of satellite tracers with expectation value $3 \times (M/10^{13} \, h^{-1}\rm M_\odot)$. This expectation value is chosen to be significantly larger than the number of satellites we typically expect in haloes of that mass (see e.g. Guo et al. 2015). We then bin all haloes and their central and satellite tracers by halo mass and whether the concentration is above or below the median. Next, we measure all cross- and autocorrelation multipole moments between all tracers in each bin. One can then show that an estimate for the galaxy number density and the multipole moments of any arbitrary galaxy–halo model are given by (13)$$\begin{eqnarray*} \hat{n}_{\rm gal} = \sum \limits _{i = {\rm c}, {\rm s}} \sum \limits _{k = 1}^{n_{\rm bins}} N_{{\rm h}, k} \langle N_i | M_k, c_k \rangle \end{eqnarray*}$$and (14)$$\begin{eqnarray*} \hat{\xi }_\ell &amp;=&amp; \hat{n}_{\rm gal}^{-2} \sum \limits _{i = {\rm c}, {\rm s}} \sum \limits _{j = {\rm c}, {\rm s}} \sum \limits _{k = 1}^{n_{\rm bins}} \sum \limits _{l = 1}^{n_{\rm bins}} \Big [ N_{{\rm h}, k} N_{{\rm h}, l} \langle N_i | M_k, c_k \rangle \nonumber \\ &amp;&amp;\times \,\langle N_j | M_l, c_l \rangle \xi _{\ell , kl}^{ij} \Big ] \, , \end{eqnarray*}$$respectively. In the above expression, Nh, k denotes the number of haloes in bin k and, for example, $\xi _{\ell , kl}^{{\rm c}{\rm s}}$ denotes the multipole moments between centrals in bin k and satellites in bin l. The above estimate $\hat{\xi }_\ell$ approaches the expectation value of ξℓ for sufficiently small halo mass bins. We find that 100 logarithmic bins in halo mass is sufficient to adequately sample all haloes with mass $M \gt 3.52 \times 10^{13} (\Omega _{\rm m}/0.3) \, h^{-1}\, \rm M_\odot$ (corresponding to 100 particles). With such a bin width of ∼0.03 dex, any biases in ξ are less than $5{{\ \rm per\ cent}}$ of the observational uncertainty for a BOSS CMASS-like sample (see Guo et al. 2015). The above method only works for a fixed value of the satellite radial profile parameter η. In practice, it suffices to tabulate correlation function for bins in η of Δlog η = 0.1 and linearly interpolate between them.