adsabs/FOCAL huggingface dataset:
The galaxy NGC 7213 benefits from a detailed SED decomposition performed by G16, which allowed us to disentangle the relative contributions of AGN and SF activity to the global IR outcome of the source, providing a characterisation of the host galaxy in terms of stellar and dust content (M⋆ and Mdust, respectively), and ongoing SF (SFR). Here, we briefly introduce the photometric data collected from the archive and the SED decomposition procedure adopted by G16. The homogenised catalogue of total fluxes, from the UV to the FIR, is presented by G16 (see also their Table A.1: the flux densities are corrected for the aperture and magnitude zero point). In the case of NGC 7213, the photometric data included in the analysis are: the U, B, V, and R bands from de Vaucouleurs et al. (1991); the near-infrared measurements from the catalogue by Jarrett et al. (2000); the IRS spectrum re-binned by Gruppioni et al. (2016) and the photometry by Gallimore et al. (2010) and Moshir et al. (1990) in the MIR; and the FIR photometry by Spinoglio et al. (2002). The adopted SED fitting code was SED3FIT5 (Berta et al. 2013), which reproduces the stellar emission and the emission of the dust heated by the stars and the torus emission simultaneously. The code used the library by Bruzual & Charlot (2003) for the stellar contribution, that of da Cunha et al. (2008) for the IR dust-emission, and the library of smooth AGN tori by Fritz et al. (2006), updated by Feltre et al. (2012). In order to limit the degeneracy among the torus parameters, in G16 the AGN configurations of obscured sources were excluded for NGC 7213 (as supported by optical observations of the source and by the X-ray spectral properties presented in Sect. 3.1.2). The best fit model and the decomposition in the different components is presented in Fig. 5. The host-stellar contribution and the dusty SF dominate over the AGN in the optical bands and in the entire IR band, respectively. While this could appear to be in contrast with the type 1/broad-line nature of the AGN, it is in agreement with the relatively weak nuclear activity observed in NGC 7213 (revealed also through the X-ray spectral analysis reported Sect. 3.1.2).
Citation: Spinoglio et al. (2002)
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A comparison between the constraints in Figure 2 and those found from the analysis of the reflection spectrum of the disk of other sources in previous studies is not straightforward because these measurements are quite sensitive to the specific source and the quality of the data, so it may be dangerous to generalize the results found from our analysis of 17 RXTE observations of LMC X-1. In general, a correlation between the estimates of a* and α13 is common even when we analyze the reflection spectrum. However, such a degeneracy can be broken when the inner edge of the disk is very close to the compact object (Tripathi et al. 2018, 2019b; Zhang et al. 2019). For example, Tripathi et al. (2019a) analyzed simultaneous XMM-Newton and NuSTAR observations of MCG–6–30–15 obtaining 





 and 





 (90% confidence level for one relevant parameter). Even if there is a correlation between these measurements of a* and α13, see Figure 6 in Tripathi et al. (2019a), we can get quite stringent constraints on both parameters. This is not the case with the analysis of the thermal component presented in this paper. When we assume the Kerr metric, observations 7, 12, and 14 of LMC X-1 give quite high spin values (see left column of Table 1), comparable to the XMM-Newton and NuSTAR observations of MCG–6–30–15. However, when we leave α13 free it is not easy to constrain a* and α13 at the same time. While the limited energy resolution of RXTE with respect to XMM-Newton may have some effect, the key-point is in the difference between the reflection spectrum and the thermal one. The former is characterized by many features, notably, but not only, the iron Kα complex around 6–7 keV. Such features help to break the parameter degeneracy, even if the reflection spectrum has several parameters to fit. The thermal spectrum, on the contrary, has quite a simple shape and there is an intrinsic degeneracy among the model parameters. This is true even when we assume the Kerr metric: it is possible to measure the black-hole spin only when we have independent estimates of the black-hole mass, distance, and inclination angle of the disk. If we want to use the continuum-fitting method to test the Kerr metric and we add a deformation parameter, the problem of degeneracy between a* and α13 should not be a surprise.
Citation: Zhang et al. 2019
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While the properties of the SLSN hosts themselves are of interest, they are most diagnostic when compared to other classes of extragalactic transient whose progenitors are better understood. To this end, we employ a comparison sample of LGRB and CCSN3 host galaxies. In principle, CCSNe should trace all core-collapse events, although the mass function means they will be dominated by stars at the lower mass end (∼8 M⊙ to ∼25 M⊙). There also remains a possibility that some very massive stars can undergo core collapse without yielding a LSNe (e.g. Smartt 2009; Ugliano et al. 2012; Kochanek 2014) such that CCSNe samples might only provide a census of lower mass core collapsing stars (e.g. 8  M*  20 M⊙). Indeed, constraints from explosion parameters have shown the majority of CCSNe to be consistent with lower mass progenitors, as opposed to more massive Wolf–Rayet stars (Cano 2013; Lyman et al. 2016) GRBs likely represent a population with rather larger initial masses (Larsson et al. 2007; Raskin et al. 2008). LGRBs are now known to be associated with the core collapse of massive stars, and broad line SN Ic are near ubiquitously associated with low-z events (where such signatures can be seen; Hjorth et al. 2012). When compared to the hosts of CCSNe they are generally smaller and of lower luminosity, consistent with an origin in galaxies of lower metallicity (Fruchter et al. 2006; Svensson et al. 2010). In relatively local examples, where spatially resolved gas phase metallicities can be obtained, these indeed appear to be lower for GRBs than for CCSNe, even in cases where the luminosity of the galaxy is relatively high (i.e. the GRB host galaxies lie off the mass–metallicity relation; Modjaz et al. 2008; Graham & Fruchter 2013). Hence, comparing the hosts of SLSNe to these events allows us to test the large-scale environments of SLSNe against those of the bulk core-collapse population and a subset which appears to derive largely from massive stars at lower metallicity, although we note that agreement on this matter is not complete (e.g. Podsiadlowski, Joss & Hsu 1992; Eldridge, Izzard & Tout 2008; Smartt 2009; Drout et al. 2011). By exploiting both LGRB and CCSN host samples we can ascertain if there is a strong metallicity dependence in SLSN production, and if this is more or less extreme than that observed in GRB hosts.
Citation: Larsson et al. 2007
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The snowlines of various volatiles (sublimation temperature Tsub ≲ 160 K) play a major role for planet formation. Beyond the snowline, the high abundances of solids allow for efficient sticking to form larger bodies, which is further enhanced by the presence of ices (e.g., Stevenson & Lunine 1988; Ros & Johansen 2013). Extensive studies have investigated the snowline in protoplanetary disks around pre-main-sequence stars similar to the nebula out of which the solar system is assumed to have formed (e.g., Lissauer 1987; Pollack et al. 1996). In such models, the water snowline is located at a radius of a few AU. It is thought that the early pre-solar nebula was hot (>1500 K), such that both volatiles and refractories (Tsub ≳ 1400 K) are in the gas phase out to larger distances (Cassen 2001; Scott 2007; Davis et al. 2014; Marboeuf et al. 2014). The evidence of such a hot solar nebula comes from the history of the refractories, but the volatile content of comets seems to indicate that a part of the disk remains cold (Bockelée-Morvan et al. 2000; Mumma & Charnley 2011; Pontoppidan et al. 2014). The evolution of the snowline due to disk and star evolution and its accretion rate clearly affects the chemical composition in the region relevant to planet formation (e.g., Lodders 2004; Davis 2005; Öberg et al. 2011b). The most relevant volatiles are the known major ice species: H2O, CO2, and CO. Observations (Meijerink et al. 2009; Zhang et al. 2013) and models (e.g., D’Alessio et al. 1998; Dullemond et al. 2007) of protoplanetary disks around pre-main sequence T Tauri stars indicate that such disks are not warm enough to have gas-phase volatiles in the midplane beyond 30 AU, as claimed in some early solar nebula models, and, for the case of H2O, a snowline of only a few AU is typically found. Higher temperatures at large radii might be achieved, but only during the deeply embedded phase of star formation when the accretion rate is high. The question remains how hot an embedded accreting disk can be when the accretion rate is high (≥10-6M⊙ yr-1, see Dunham et al. 2014, for a recent review).
Citation: Davis et al. 2014
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There are still several sources of systematics we do not consider in this paper. For instance, whether the use of a different mass distribution models for these lenses could significantly affect the final result. Therefore, we performed a sensitivity analysis and repeated the above calculation using the extend power law (EPL) lens model, in which the luminosity density profile [v(r) ∼ r−δ] is different from the total mass (luminous plus dark matter) density profile ρ(r) ∼ r−α. Such lens model has found widespread astrophysical applications in the literature (Cao et al. 2016a; Xia et al. 2017; Qi et al. 2019), considering the anisotropic distribution of stellar velocity dispersion β (Koopmans 2005; Cao et al. 2017b; Chen et al. 2019). With the EPL lens parameters (α, β, δ) modelled by Gaussian distributions α = 2.00 ± 0.08, δ = 2.40 ± 0.11, and β = 0.18 ± 0.13 (Gerhard et al. 2001; Bolton, Rappaport & Burles 2006; Graur et al. 2014; Schwab, Bolton & Rappaport 2010), the scatter plot of the deviation Tzs in EPL model is shown in Fig. 4. Our results provide the deviation Tzs = 0.974 ± 0.017 [corresponding to c(zs) = 2.922(± 0.051) × 105 km s−1], and the median value Med(Tzs) = 0.983 with the median absolute deviation MAD(Tzs) = 0.259 for the full lens sample. Therefore, our results show that the assumed lens model has a slight impact on the SOL constraint, which highlights the importance of auxiliary data (such as more high quality integral field unit) in improving constraints on the density profile of gravitational lenses. More detailed models of mass distribution, such as Navarro–Frenk–White density profile (suitable for dark matter distribution; Navarro, Frenk & White 1997), Sersic-like profile (suitable for stellar light distribution) (Sérsic 1968), pseudo-isothermal elliptical mass distribution, could also be considered in this context (Kassiola & Kovner 1993). However, strong lensing observables we used are determined by the total mass inside the Einstein radius. Hence, they are not so sensitive to the details of the very central distribution like cusps (besides the extremal cases). Moreover, the Einstein rings of the lenses we used corresponded to less than 10 kpc hence the NFW profile of the dark halo would not likely be manifested.
Citation: Qi et al. 2019
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Herschel imaging observations have shown that filamentary structures are truly ubiquitous in the cold interstellar medium (ISM) of the Milky Way (Molinari et al. 2010), dominate the mass budget of Galactic molecular clouds at high densities (≳104 cm−3) (Schisano et al. 2014; Könyves et al. 2015), and feature a high degree of universality in their properties. In particular, detailed analysis of the radial column density profiles indicates that, at least in the nearby clouds of the Gould Belt, molecular filaments are characterized by a narrow distribution of crest-averaged inner widths with a typical full width at half maximum (FWHM) value Wfil​​ ∼ ​​0.1 pc and a dispersion of less than a factor of ∼2 (Arzoumanian et al. 2011, 2019; Koch & Rosolowsky 2015). Another major result from Herschel (e.g., André et al. 2010; Könyves et al. 2015; Marsh et al. 2016) is that the vast majority (> 75%) of prestellar cores are found in dense “transcritical” or “supercritical” filaments for which the mass per unit length, Mline, is close to or exceeds the critical line mass of nearly isothermal, long cylinders (e.g., Inutsuka & Miyama 1997), 




M

line,crit

​
=
 ​
2

c
s
2

/G~16
M
⊙



$ {M_{{\rm{line}},{\rm{crit}}}} = \,2{\mkern 1mu} c_{\rm{s}}^2/G{\mkern 1mu} \sim {\mkern 1mu} 16{\mkern 1mu} {M_ \odot } $


 pc−1, where cs​ ∼ ​0.2 km s−1 is the isothermal sound speed for molecular gas at T​ ∼ ​10 K. Moreover, most prestellar cores lie very close to the crests (i.e., within the inner 0.1 pc portion) of their parent filaments (e.g., Könyves et al. 2019; Ladjelate et al. 2019). These findings support a filamentary paradigm in which low-mass star formation occurs in two main steps (André et al. 2014; Inutsuka et al. 2015): (1) multiple large-scale compressions of cold interstellar material in supersonic magneto-hydrodynamic (MHD) flows generate a cobweb of ∼0.1 pc-wide filaments within sheet-like or shell-like molecular gas layers in the ISM and (2) the densest molecular filaments fragment into prestellar cores (and then protostars) by gravitational instability near or above the critical line mass, Mline, crit, corresponding to 




Σ

gas

crit

∼

M

line
,
crit


/

W
fil

∼
160


M
⊙



$ \Sigma_{\mathrm{gas}}^{\mathrm{crit}} \sim M_{\mathrm{line, crit}}/W_{\mathrm{fil}} \sim 160\,M_\odot $


 pc−2 in gas surface density (AV ∼ 7.5) or nH2 ∼ 2 × 104 cm−3 in volume density. This paradigm differs from the classical gravo-turbulent picture (Mac Low & Klessen 2004) in that it relies on the anisotropic formation of dense structures (such as shells, filaments, cores) in the cold ISM and the unique properties of filamentary geometry (see Larson 2005).
Citation: Arzoumanian et al. 2011
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In Sifón et al. (2013), we used the σ–M200 scaling relation of Evrard et al. (2008) to estimate dynamical masses. As discussed in Section 1, the scaling relation of Evrard et al. (2008) was calibrated from a suite of N-body simulations using DM particles to estimate velocity dispersions. However, the galaxies, from which velocity measurements are made in reality do not sample the same velocity distribution as the DM particles. They feel dynamical friction and some are tidally disrupted, which distorts their velocity distribution and biases their dispersion (e.g. Carlberg 1994; Colín et al. 2000). Recent high-resolution hydrodynamical simulations of ‘zoomed’ cosmological haloes have shown that there is a significant difference between the velocity distributions of DM particles and galaxies themselves; whether galaxies (i.e. overdensities of stars in hydrodynamical simulations) or DM subhaloes are used makes comparatively little difference (Munari et al. 2013). Results from state-of-the art numerical simulations depend on the exact definition of a galaxy and the member selection applied, but the current consensus is that galaxies are biased high (i.e. at a given mass the velocity dispersion of galaxies or subhaloes is larger than that of DM particles) by 5–10 per cent with respect to DM particles (Lau et al. 2010; Munari et al. 2013; Wu et al. 2013), translating into a positive 15–20 per cent bias in dynamical masses when using DM particles. This is illustrated in Fig. 5: DM particles are not significantly impacted by either dynamical friction or baryonic physics; therefore, the scaling relations for DM particles are essentially the same for all simulations. In contrast, DM subhaloes are affected by baryons in such a way that including baryonic feedback (most importantly feedback from active galactic nuclei – AGN, but also from cooling and star formation) makes their velocity dispersions much more similar to those of simulated galaxies. This means we can rely on our analysis of the previous section, based on DM subhaloes, to correct the velocity dispersions measured for ACT clusters, and then estimate dynamical masses using predictions obtained either from galaxies or subhaloes. The difference between the Saro et al. (2013) and Munari et al. (2013) galaxy scaling relations depends on the details of the semi-analytic and hydrodynamical implementations used in Saro et al. (2013) and Munari et al. (2013), respectively. The different cosmologies used in the Millenium simulation (in particular, σ8 = 0.9; Springel et al. 2005) by Saro et al. (2013) and the simulations by (Munari et al. 2013, σ8 = 0.8) may also play a role.
Citation: Munari et al. 2013
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Citation: Munari et al. (2013); Munari et al. (2013)
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Citation: Munari et al. 2013
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When the size of the logarithmic redshift bin is small enough, we can use fluxes in place of luminosities, performing a test on the (non-)evolution with redshift that is completely independent from any assumption on cosmology. Risaliti & Lusso (2019) analysed in detail the choice of the bin size and verified that, as long as Δlog(z)≤0.1, the slope in the relation does not depend on it. Thanks to the statistics available, we chose Δlog(z)=0.06 and we limited our analysis to the redshift bins with more than five objects. We performed the same analysis for bins of size Δlog(z)=0.05 and Δlog(z)=0.07, finding no significant difference (see Fig. 9 for a comparison of the results for different sizes of the redshift bins). For our selection in Γ and the choice of the threshold for the Eddington bias, the division yielded 17 redshift bins. To perform the fitting to the data, we adopted the Python package emcee (Foreman-Mackey et al. 2013), a pure-Python implementation of Goodman & Weare’s Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler. To check that the results were independent from the employed method, we also performed the analysis using the Linmix package (Kelly 2007), an algorithm that makes use of a Bayesian approach to linear regression and takes into account the errors in both the x and y variable. We performed the fit a first time, then applied a 3σ clipping to the data, repeating this sequence for a total of three times. This yielded no significant difference with respect to the analysis without σ clipping. The results are shown in Figs. 7 and 9, and summarised in Table 3. In Fig. 7, red points indicate when the observations are characterised by an SN  5 in the soft band. Most of them are (X-ray) fainter objects at intermediate redshifts. This confirms that data points that passed our selection criteria, even if with a low SN, follow the relation and are representative of the population of blue quasars.
Citation: Foreman-Mackey et al. 2013
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Figure 6 compares the IR SED of SN 2014C with the SEDs of all other interacting SNe for which observations beyond 5 μm are available in the literature. Apart from SN 2014C, only four other strongly interacting SNe IIn have been observed in the mid-IR. Three of these were observed at epochs comparable to the epoch at which SN 2014C was observed in the mid-IR. SN 2005ip was observed at 936 days post-explosion during the cryogenic phase of Spitzer. It was the only interacting SN with an InfraRed Spectrograph (IRS; Houck et al. 2004) mid-IR spectrum from 5 to 12 μm and IRAC photometry at 5.8 and 8 μm (Fox et al. 2010). SN 2006jd was observed at 1638 days, an epoch very similar to that of SN 2014C, with Spitzer/IRAC and WISE (Stritzinger et al. 2012). SN 2010jl was observed at 1279 days with Spitzer/IRAC and SOFIA/FORCAST (Herter et al. 2018) at 11.1 μm, resulting in a deep upper limit after 6400 s of total integration time (Williams & Fox 2015). In all three cases, the photometry and spectra from 1 to 10 μm are well fitted by purely carbonaceous dust models, which we overplot in Figure 6 using dust parameters from the literature. Lastly, SN 1995N was observed at more than 10 yr post-explosion by Spitzer/IRAC and WISE (Van Dyk 2013). Its SED shape differs markedly from those of the other three SNe observed at earlier epochs. The carbonaceous dust model that Van Dyk (2013) fitted to the data is shown in Figure 6. However, we note that the shallow slope from 3 to 10 μm cannot be fitted with a single-temperature dust model, regardless of composition, and would require a range of dust temperatures. Given the late epoch of the observation, one might also consider a nonthermal origin for the IR emission from SN 1995N, as the SED can also be described with a broken power law (Fν ∝ ν−3; also overplotted) with a knee at around 12 μm. In addition to these H-rich interacting SNe, the H-poor interacting SN 2006jc (Ibn) was also observed beyond 5 μm with AKARI  (Sakon et al. 2009). Its SED was also best fitted with a two-temperature amorphous carbon dust model. This comparison highlights SN 2014C’s unique SED shape among other interacting SNe for which data are available beyond 5 μm at comparable epochs, showing for the first time an evidence for a silicate dust feature in the IR SED of an interacting SN. It also accentuates the need for observations of interacting SNe at late times, out to decades post-explosion, in the near- to mid-IR, which will be enabled by the upcoming James Webb Space Telescope (JWST).
Citation: Stritzinger et al. 2012
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RX J1347.5–1145 is one of the most luminous X-ray galaxy clusters and is located at a redshift of z = 0.451. It was thought to be a relaxed cluster when it was discovered in the ROSAT all sky survey (Schindler et al. 1997). Komatsu et al. (1999) made the first measurements of the Sunyaev–Zel’dovich effect (SZE: Sunyaev & Zeldovich 1972) toward this cluster with the James Clerk Maxwell Telescope at 350 GHz as well as with the 45 m Nobeyama Radio Telescope at 21 and 43 GHz. A higher angular resolution observation of the SZE was performed by Komatsu et al. (2001) using the Nobeyama Bolometer Array and they found a prominent substructure which has no counterpart in the soft X-ray image from ROSAT. The presence of the substructure has been confirmed by Chandra and XMM-Newton (e.g., Allen et al. 2002; Gitti & Schindler 2004) as well as by more recent SZE measurements (Mason et al. 2010; Korngut et al. 2011; Plagge et al. 2013; Adam et al. 2014; Kitayama et al. 2016). Allen et al. (2002) measured the mean temperature of the ICM to be over 10 keV, which is relatively high compared to other typical clusters. Kitayama et al. (2004) and Ota et al. (2008) found a very hot (>20 keV) component of the ICM in this cluster. In addition, the radial profile and spatial distribution of the ICM temperature indicate that the temperature drops to ∼6 keV toward the cluster center so that the cool core is formed (e.g., Allen et al. 2002; Ota et al. 2008; Kreisch et al. 2016). A disturbed morphology is further supported by radio synchrotron observations (e.g., Ferrari et al. 2011) and gravitational lensing maps (e.g., Köhlinger & Schmidt 2014). The total mass of RX J1347.5–1145 within r200 is estimated to be ∼1.5 × 1015 h−1 





  using weak-lensing analysis, where r200, the radius within which the mean mass density is 200 times the critical density of the universe, is 1.85 h−1 Mpc (Lu et al. 2010) for this galaxy cluster.18

18
They adopted the Hubble constant of 70 km s−1 Mpc−1.
Citation: Plagge et al. 2013
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Citation: Korngut et al. 2011
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