adsabs/FOCAL huggingface dataset:
For comparisons with the light curve YORP constraints, the YORP effect acting on Cuyo could be predicted by computing the total recoil forces and torques from reflected and thermally emitted photons from the asteroid surface using the ATPM. These calculations were made for both a smooth and rough surface, and were averaged over both the asteroid rotation and elliptical orbit (see Rozitis & Green 2012, 2013, for methodology). As demonstrated in Rozitis & Green (2012), the inclusion of rough-surface thermal-infrared beaming effects in the YORP predictions tends to dampen the YORP rotational acceleration on average but can add uncertainties of up to several tens of per cent if the roughness was varied across the surface. Since the light curve inversion produced convex shape models only, then shadowing and self-heating effects inside global-scale concavities (see Rozitis & Green 2013) were not possible to model. However, a study of non-convex shape models for fast two to four hour rotators in Rozitis & Green (2013) indicated that such asteroids have rather minimal levels of global-scale concavities, and the ~ 2.7 h rotation period of Cuyo implies that its shape could be similar. Furthermore, the Tangential-YORP effect, that is, a predicted rotational acceleration caused by temperature asymmetries within exposed rocks and boulders on the surface of an asteroid (Golubov & Krugly 2012), was also not included in the ATPM predictions. However, the very low thermal inertia value measured for Cuyo implies the absence of rocks and boulders on its surface of the quantity and size that are necessary to induce a significant Tangential-YORP component. As Cuyo is likely to be an S-type rubble-pile asteroid, a bulk density equivalent to that measured for the S-type rubble-pile asteroid (25143) Itokawa (Abe et al. 2006) of 2 g cm−3 was assumed for the YORP computations. Using the thermo-physical properties derived earlier, the ATPM predicts YORP rotational acceleration of (−6.39 ± 0.96) × 10−10 rad day−2 for the nominal shape model. The uncertainty given here corresponds to the standard deviation of results when the degree of surface roughness israndomly varied across the surface of Cuyo (see Lowry et al. 2014, for details of the Monte Carlo methodology used). These values lie well within the light curve rotational acceleration constraints determined previously.
Citation: Rozitis & Green; 2013
Citation function: Uses; Uses
As described in Sect. 1, cloudshine is observed in the near-IR, usually in the three photometric bands J, H, and K. For instance, using the Wide Field CAMera of the United Kingdom InfraRed Telescope (WFCAM of UKIRT), Malinen et al. (2013) observed a 1° × 1° field in the Taurus Molecular Complex (d ~ 140 pc). This field is centred on the dense TMC-1N cloud (RA (J2000) 4h39m36s and Dec (J2000) + 26°39′22′′) north of TMC-1. After smoothing the data to 40″ resolution on a 8″ pixel grid and removing background emission, Malinen et al. (2013) were able to extract J, H, and K coreshine median radial profiles across the TMC-1N filament (see their Fig. 12). This filament was previously observed with PACS and SPIRE instruments on board Herschel: using the colour temperature of the dust submm emission and assuming that the dust opacity varies as 0.1 cm2/g (ν/ 1000 GHz)β with β = 2, Malinen et al. (2012) found that the density distribution of the cloud is accurately described by a Plummer-like function with ρC = 4 × 104 cm-3, Rflat = 0.03 pc, and p = 3 (see Eq. (1)). To model this cloud, we start from this density distribution and the dust population mixtures presented in Sect. 4.1. The cloud is then illuminated by the ISRF+CM radiation field, extinguished or not by a layer of CM grains with \hbox{$A_V^{\rm ext} = 0.5$}AVext=0.5 to 3 (see Fig. 5). The resulting dust scattered emission maps4 are finally smoothed to the same resolution as the data presented by Malinen et al. (2013). The best fit is achieved for the CMM+AMMI populations illuminated by the ISRF+CM radiation field with \hbox{$A_{{V}}^{\rm ext} = 1.5^{{\rm mag}}$}AVext=1.5mag. The results are presented in Fig. 11. As Malinen et al. (2013) subtracted the cloud background, it is not surprising to find \hbox{$A_{{V}}^{\rm ext} \neq 0$}AVext≠0; it probably reflects the fact that the incident radiation field is extinguished by the removed envelope before reaching the dense cloud. The peak intensity and the profile shapes are reproduced well for the three bands, even if the modelled profiles for the J and K-bands are slightly broader than the observed values.
Citation: Malinen et al. (2012)
Citation function: Background; Uses; Uses
Lastly, we model the evolution of the ejecta discussed in Section 2.2 into circumstellar profiles discussed in Section 2.3. We use our cosmic-ray hydrodynamics code, hereafter called ChN to model the evolution of the ejecta to an age of 






t


SNR


=
400


 yr. ChN is a Lagrangian hydrodynamics code that includes a prescription for diffusive shock acceleration (DSA; Ellison et al. 2007; Lee et al. 2012). We have modified the code to include the effects of DSA on non-equilibrium ionization (Patnaude et al. 2009, 2010) and have coupled the code to SN ejecta models (Lee et al. 2014; Patnaude et al. 2015). We have also included radiative losses via forbidden line cooling (Lee et al. 2015). This effect will be important in the evolution of the SN shock with a nearby CSM shell, or if we choose to model the radiative shock that could form in the ejecta during early SN evolution (Nymark et al. 2006). However, we begin our simulations at an age of 5 yr, and over the lifetime of the simulation the shocks remain adiabatic, so we do not consider the radiative shock model presented in our previous work here. Since ChN couples nonlinear particle acceleration to the SNR shock dynamics, we are able to reproduce the broadband thermal and nonthermal emission (Ellison et al. 2010, 2012; Castro et al. 2012; Slane et al. 2014; Lee et al. 2013). The diffusive shock acceleration process is an integral part of ChN, and some injection of thermal particles into the acceleration process is always assumed. Here we set the injection parameter to the test particle limit, though we note that the interaction of a strong shock with a massive CSM shell or cloud will lead to enhanced particle acceleration (e.g., Ellison et al. 2012; Lee et al. 2014), and the differing CSM configurations, combined with the differing ejecta profiles and compositions, may result in differences in the broadband nonthermal emission. The study of nonthermal emission in evolving SNe is sufficiently broad that we defer its study to future papers.
Citation: Lee et al. 2014
Citation function: Compare/Contrast; Compare/Contrast
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 &amp;=&amp; \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.
Citation: Machida & Hosokawa 2013
Citation function: Uses
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.
Citation: Vuitton et al. 2007
Citation function: Compare/Contrast
Although in the MIR, the dust models are more absorbant than the measured samples, the opposite is true in the FIR. Table 2 gives the value of the MAC of the measured samples and of the modeled MAC at selected wavelengths in the 100 μm−1 mm range. In this range, the experimental MAC at 300 K is more than five times greater than the modeled MAC for all samples. At 10 K, the experimental MAC is lower than at 300 K and the factor of enhancement compared to the models is smaller, depending on the sample and on the wavelength, however it is always higher than two and usually of the order of four to five. The MAC value of the ferromagnesium silicate analogues in this study is very close the MAC of the pure Mg-rich samples from Demyk et al. (2017) and from Coupeaud et al. (2011). This shows that the enhancement of the measured MAC compared to the modeled MAC is not related to the iron content of the grains, that is, to differences in composition between the studied samples and the analogues used in the cosmic dust models. As discussed in detail by Demyk et al. (2017), an enhancement factor greater than two cannot be explained by the effect of grain size, grain shape, or by grain coagulation within the pellets. Grain size and grain shape effects are illustrated in Fig. 10 and the increase of the MAC due to large (micronic) spherical grains is negligible in the FIR. Coagulation might happen during the process of fabrication of the pellets and it is taken into account during the analysis of the experimental data following the method explained in Mennella et al. (1998) and based on the Bruggeman theory. More detailed treatment of dust coagulation by methods such as DDA have shown that it may increase the MAC by a factor of two at most (Köhler et al. 2011; Mackowski 2006; Min et al. 2016), that is, not enough to fully account for the discrepancy of the cosmic dust models and the experimental data. The enhancement of the emissivity of iron-rich analogues compared to the MAC of cosmic dust models commonly used for interpreting FIR/submm dust emission observations is related to the disordered nature of the samples, to the number, distribution, and nature of defects in their structure at microscopic scale and to the existence of absorption processes added to the classical Debye model (we refer to Demyk et al. 2017, for more details). These additional absorption processes are more or less important depending on the structural state of the material at the microscopic scale.
Citation: Coupeaud et al. (2011)
Citation function: Similarities
A number of candidate TDEs were discovered by UV and X-ray surveys (e.g. NGC5905; Komossa & Greiner 1999, RX J1624+75; Grupe et al. 1999, A1795; Donato et al. 2014, Swift J164449.3+573451; Bloom et al. 2011; Burrows et al. 2011; Levan et al. 2011; Zauderer et al. 2011, Swift J0258.4+0516; Cenko et al. 2012b, and GALEX candidates D1–9, D3–13, and D23H-1; Gezari et al. 2008, 2009). Many of these cases did not show correspondingly strong optical emission, and in some cases they were discovered in sparse archival data, limiting their usefulness as probes of TDE physics. In recent years, however, a number of candidates have been found by high-cadence optical surveys, including the ASASSN-14ae (Holoien et al. 2014a), the Palomar Transient Factory (PTF10iya, Cenko et al. 2012a; PTF09ge, PTF09axc, and PTF09djl, Arcavi et al. 2014), Pan-STARRS (PS1-10jh, Gezari et al. 2012; PS1-11af, Chornock et al. 2014), and the Sloan Digital Sky Survey (SDSS; TDE 1 and TDE 2; van Velzen et al. 2011). These transients typically showed strong UV and optical emission but no associated X-ray emission, and were often better-studied than previous candidates, as they were discovered and followed up by survey projects searching their data in real-time. Optically discovered candidates have provided new opportunities to study these rare transients in greater detail than previously possible, and recent estimates based on optical TDE discoveries put the TDE rate at $\dot{N}_{\text{TDE}}=$ (1.5–2.0)$^{+2.7}_{-1.3}\times 10^{-5}$ yr−1 per galaxy (van Velzen & Farrar 2014). However, there is still tension between this estimate and the rates predicted by modelling two-body scattering of stars in galactic nuclei (typically ∼10−4 yr−1 per galaxy), which may in part be due to the fact that only a small fraction of TDEs are optically luminous (Stone & Metzger 2014; Metzger & Stone 2015). Selection effects from optical surveys may also play a role in this discrepancy, and a careful determination of TDE detection efficiency and completeness is needed to determine the degree to which the actual TDE rate may be higher than the rates inferred from optical surveys.
Citation: Cenko et al. 2012a
Citation function: Motivation; Motivation; Motivation
AstroSat data enabled us to derive the X-ray spectrum in the 0.8–70 keV energy band as shown in Figure 7. The spectrum was fitted reasonably well using two models. The first model was defined as an absorbed Fermi–Dirac cutoff model along with a blackbody, an Fe emission line, and three Gaussian absorption lines introduced to model cyclotron scattering features and its two higher harmonics as observed in the spectrum. The CompTT model was used as the second model in combination with an Fe emission line and three Gaussian absorptions lines as defined in the first model. The CompTT model is generally used for neutron-star-based low-mass X-ray binaries such as Z-type and atoll sources with a relatively lower magnetic field (





 G) of the neutron star (Ferinelli et al. 2008). However, the model could also successfully define the spectrum of some of the pulsars in Be binaries, for example, Cep X-4 (Jaiswal & Naik 2015), 4U 1907+09 (Varun et al. 2019), and GRO J2058+42 (Molkov et al. 2019). The parameters derived from the two models are tabulated in Table 2. The first model estimated a blackbody temperature of 0.83 ± 0.04 keV and detected the presence of a cyclotron resonance scattering feature and its harmonics. The Comptonization model, on the other hand, enabled us to determine the input photon Wien temperature of 0.52 ± 0.02 keV, the plasma temperature of 8.22 ± 0.10 keV, and the plasma optical depth of 5.21 ± 0.12 for the phase-averaged spectra. The Wien temperature, per the CompTT model, originates far from the neutron star surface and closer to the inner accretion disk i.e., at the outer transition layer; hence, the Wien temperature is always found to be relatively lower than the neutron star blackbody temperature as it originates closer to the inner transition layer, i.e., near the surface of the neutron star (Ferinelli et al. 2008). For the CompTT model, bulk Comptonization occurs in the innermost part of the transition layer region, while thermal Comptonization is dominant in the outer transition layer and presumably within some extended region located above the accretion disk. Similar deviations were also observed in the case of Cep X-4 fitted with the CompTT model and a blackbody combined with the FDCUT model (Table 1 of Jaiswal & Naik 2015). However, the centroid energy of the cyclotron absorption features and its detected harmonics are found to be consistent within errors for the two models (Table 2).
Citation: Molkov et al. 2019
Citation function: Compare/Contrast; Compare/Contrast
In our calculation, we introduce two dust components: small dust grains and large dust grains. The minimum and maximum sizes are 0.0025 and 0.2 μm for small dust grains and 0.01 μm and 1 mm for large dust grains, respectively. The size distribution of both dust components is n(a) ∝ a−3.5, where n(a) is the number density per unit radius and a is the radius of dust grains. The opacity models for both small and large dust grains are described in Hashimoto et al. (2015) (see also Kim et al. 1994; Wood et al. 2002; Dong et al. 2012). We adopt Equation (16) as the surface density distribution of large dust grains and assume that the surface density of the small dust grains is 10 times smaller than that of the large dust grains (Σsmall = 0.1Σlarge; e.g., D’Alessio et al. 2006). We note that the small-to-large dust mass ratio for the size distribution of n(a) ∝ a−3.5 is 1%. However, many practical calculations (e.g., Andrews et al. 2011; Dong et al. 2015b) use the size distribution and change the small-to-large dust mass ratio independently because the results are not very different even if the mass ratio is assumed as 10%. To obtain the density distributions of small and large dust grains, we set the scale height of them,
17







18





where Hl and Hs are the scale heights of large and small dust grains, respectively. We assume that the scale height of small dust grains is similar to the gas scale height. The scale height of the large dust grains is assumed to be about three times smaller than that of the small dust grains.15

15
If we assume that the gas surface density is 10 g cm−2 and turbulence strength α = 10−2 at 70 au, we obtain that the scale height of 1 mm dust, which is the maximum size of the large dust, is about three times smaller than the gas scale height. In reality, this ratio will depend on the radius, but here we assume the constant ratio for simplicity.
 Using these scale heights, we evaluate the density of small and large dust grains, whose density distributions in the z-direction are proportional to 





. We note that the dependence of model parameters on the resulting disk structures is beyond the scope of this work and remains to be explored.
Citation: D’Alessio et al. 2006
Citation function: Uses
To infer starburst and AGN luminosities, we use the multicomponent SED fitting technique described in Hatziminaoglou et al. (2008) and Hatziminaoglou, Fritz & Jarrett (2009). In the most general case, the method fits fluxes and their errors with three separate models: stellar, AGN, and starburst. Our sample however consists entirely of luminous type 1 quasars with extremely high SFRs. This allows us to make two key simplifications to the general method. First, it is almost certain that the stellar emission will be outshone by the AGN and/or the starburst at all wavelengths, so we do not include a stellar template in the fit. Second, since our sources are type 1 quasars, it is reasonable to conclude that the optical to mid-infrared (MIR) coverage will be dominated by the AGN (Hatziminaoglou et al. 2005), and that the FIR coverage will be dominated by the starburst (e.g. Hatziminaoglou et al. 2010; Ma & Yan 2015; Harris et al. 2016). The infrared emission reprocessed by the AGN torus strongly depends on the dust geometry. In particular, the ratio between the outer and inner radius of the torus plays a major role in determining the AGN SED at longer wavelengths (in the models used in this paper the maximum value of this ratio is 150). As a result, the peak of the infrared emission of the AGN torus models lies at 10–30 μm (fig. 5 of Feltre et al. 2012) and the maximum width of the infrared bump (as defined in Feltre et al. 2012) for these models is ∼50 μm. We therefore do not expect any significant contribution from the AGN emission at wavelengths larger than ∼50 μm. Complementary, observationally motivated discussion on this point can be found in section 5.3 of Harris et al. (2016). We note the caveat though that, with our data, which do not cover the range between 22 and 250 μm, we cannot completely rule out significant FIR emission from the AGN. We thus use the shorter wavelength data to constrain the properties of the AGN, and, having done so, then use the SPIRE data to constrain the properties of the starburst. Two example SED fits are given in Fig. 3; one for a classical quasar and one for a BAL quasar.
Citation: Ma & Yan 2015
Citation function: Uses; Uses