adsabs/FOCAL huggingface dataset:
Thanks to their short spectral lags, cosmological distances, and very high-energy photons, GRBs have been viewed as the most promising sources for studying  LIV effects (Amelino-Camelia et al. 1998; Ellis et al. 2006; Jacob & Piran 2008). In the past, various limits on LIV have been obtained from the spectral time lags of individual GRBs or a large sample of GRBs (e.g., Amelino-Camelia et al. 1998; Coleman & Glashow 1999; Schaefer 1999; Ellis et al. 2003, 2006; Boggs et al. 2004; Kahniashvili et al. 2006; Jacob & Piran 2008; Abdo et al. 2009a, 2009b; Biesiada & Piórkowska 2009; Xiao & Ma 2009; Shao et al. 2010; Chang et al. 2012, 2016; Nemiroff et al. 2012; Ellis & Mavromatos 2013; Kostelecký & Mewes 2013; Vasileiou et al. 2013, 2015; Pan et al. 2015; Zhang & Ma 2015; Wei et al. 2016). In particular, Abdo et al. (2009a) used the time lag of the highest energy (13.2 GeV) photon from GRB 080916C to constrain the linear LIV energy scale (






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 GeV, improving the previous limits by at least one order of magnitude. Abdo et al. (2009b) set the current strictest limits on both the linear and quadratic LIV energy scales by analyzing the arrival time delay between a 31 GeV photon and the low-energy (trigger) photons from GRB 090510. The limits set are 






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 GeV. However, these limits were based on the rough time lag of a single GeV-scale photon. It is necessary to consider using the true spectral time lags of bunches of high-energy photons (i.e., the lags of high-quality, high-energy light curves) to constrain the LIV. Furthermore, since the emission mechanism of GRBs is still poorly understood, it is difficult to distinguish an intrinsic time delay at the source from a delay induced by propagation in a vacuum to the observer. That is, the method of the flight-time difference used for testing LIV is hindered by our ignorance concerning the intrinsic time delay in different energy bands (see, e.g., Ellis et al. 2006; Biesiada & Piórkowska 2009).
Citation: Chang et al. 2012
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The differences in composition are reflected in the final abundances after r-process nucleosynthesis, shown in Fig. 15. The abundances are obtained using a grid of pre-computed trajectories with SkyNet (Lippuner & Roberts 2017), as discussed in detail in Radice et al. (2018b). We normalize the relative abundances by fixing the height of the third r-process peak (A ≃ 190). We also report Solar r-process abundances from Arlandini et al. (1999) in the same figure. However, we emphasize that even if NS mergers were the sole contributor of r-process elements, there is no reason to expect that every merger should produce ejecta with relative abundances close to Solar. Indeed, variability between the yields of different mergers is required to explain observed abundances in metal-poor stars (Holmbeck et al. 2019). Overall, the simulations span a factor ∼2 in the ratio of A ≃ 100 to third r-process peak. However, the difference between the M0 and M1 at the SR resolution, which is the resolution we use for production simulation, are modest compared to the systematic uncertainties from the unknown NS EOS and to the variability due to the binary mass ratio (Radice et al. 2018b; Nedora et al. 2021b). Clearly, strong conclusions cannot be drawn from this limited study alone, but our simulations suggest that the uncertainties in the yields from mergers arising from neutrino radiation treatment are modest. This is also supported by the results of Foucart et al. (2020). They compared M1 and Monte Carlo neutrino transport in the context of NS mergers and reported only a modest ${\sim }10{{\ \rm per\ cent}}$ difference in the Ye of the ejecta between the two schemes. Interestingly, they reported that M1 systematically overestimates the Ye of the ejecta, so we cannot exclude that the M0+Leakage results are actually more accurate than the results obtained with THC_M1. That said, it is important to emphasize that this comparisons has only been made for the dynamical ejecta and not for the secular ejecta, which we discuss in Section 6.5.
Citation: Holmbeck et al. 2019
Citation function: Uses
Theoretical single-ionization cross-sections for the Ar2+ ion are compared to the experimental data in Fig. 2. The cross-sections for other two levels, 3P1 and 3P0, are in close agreement with the values from 3P2 and are therefore not presented. The cross-sections of the direct process are evaluated in the potential of the ionized ion. Experimental data obtained by Diserens et al. (1988) and Man et al. (1993) are only presented in Fig. 2. These experimental data showed the lowest contribution from the metastable fraction in the ion beam. Measurements from Muller et al. (1980), Danjo et al. (1984), Mueller et al. (1985), and Matsumoto et al. (1990) demonstrated onsets below the ionization threshold. In addition, the CADW calculations (Loch et al. 2007) are presented for comparison in Fig. 2. The DW cross-sections for the ground level are in good agreement with the experimental results at low energies (Diserens et al. 1988; Man et al. 1993). However, the calculations are below the measurements at the high energies. The theoretical cross-sections are above the experimental values for the two highest levels of the ground configuration from the ionization threshold up to 200–300 eV, which is well beyond the peak region. On the other hand, good agreement with the measurements for these two levels is obtained at the higher energies. It should be noted that the CADW cross-sections (Loch et al. 2007) underestimate the measurements at the low electron energies. Excitations from the 2p subshell are investigated in the CADW calculations of these latter authors, but it is not clear whether the EA channels corresponding to the excitations from the 3s and 3p subshells are included in their study. On the other hand, decays of the excited Ar2+ 2p 53s 23p4 nl (n ≥ 4, l n) configurations lead to states of the Ar4+ ion. The total excitation cross-sections from the 2p subshell amount to ~1 Mb at the peakenergy of 280 eV in our study. Therefore, the excitations from the 2p subshell are not investigated in this work.
Citation: Matsumoto et al. (1990)
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We now analyse whether release of this entrapped CO is possible, and quantify it using simple assumptions. In the Fomalhaut ring, the rate at which large planetesimals in the belt are being ground down, which is the same as the rate at which small dust grains are being replenished, has been estimated as 6.0 × 1017 kg yr−1 (Wyatt & Dent 2002). Assuming the planetesimal composition to be similar to that observed in Solar system comets (ice/rock ratio of about 1, CO/H2O ice ratios between 0.4 and 30 per cent), we can use it to estimate the rate of CO release, which corresponds to between 1.9 × 1015 and 8.0 × 1016 kg yr−1. The mechanisms that can be responsible for this release are collisions and photodesorption. Collisions can contribute through dust vaporization (Czechowski & Mann 2007), planetesimal breakup (Zuckerman & Song 2012) and giant impacts (Jackson et al. 2014). Photodesorption, on the other hand, will affect the H2O ice on the surface of solids, in turn exposing CO and allowing it to escape on very short time-scales. The rate at which water vapour will be released is da/dt ∼ 1.5 × 10−3 μm yr−1 (scaled to the distance of the ring, from the result of Grigorieva et al. 2007), where a is the vertical thickness of the layer. The total water mass released in this manner will then be

(19)

\begin{equation}
\frac{\mathrm{d}M_{\rm H_2O}}{\mathrm{d}t} = 4\sigma _{\rm tot}\rho _{\rm gr} \frac{\mathrm{d}a}{\mathrm{d}t},
\end{equation}

where ρgr is the grain density, which we take as that of water ice (∼1 × 10−15 kg μm−3) and σtot is the total cross-section of icy grains in the Fomalhaut ring (in μm2). Under the assumption that all the grains are fully icy, we can use the total cross-sectional area of the Fomalhaut ring (33.7 au2; Wyatt & Dent 2002) to obtain an H2O production rate of 4.6 × 1018 kg yr−1. This is higher than the rate at which mass is being passed down the collisional cascade (again, 6.0 × 1017 kg yr−1), pointing towards a more realistic scenario where grains are not fully icy, but made up of a mixture of ice and rock. We therefore conclude that photodesorption of H2O might contribute significantly to the release of trapped CO gas from planetesimals in the ring, though the extent of this contribution depends on how much of the cross-sectional area of the Fomalhaut disc is icy. In any case, the exact mechanism for CO gas production is unimportant as long as there is one that can feasibly explain its release.
Citation: Grigorieva et al. 2007
Citation function: Uses; Uses
Although the method developed here is not identical to the means by which SONG determines radial velocity from the observed spectra, it simulates the SONG observations sufficiently well. First, the set of fictitious Fe i lines carefully chosen in this work is able to represent the properties of most Fe i lines seen between 4400 and 6900 Å, which constitute a large part of all lines in this wavelength interval. Secondly, the procedure to extract radial velocity from theoretical spectral lines is similar to how radial velocities are typically obtained from observed spectra. Thirdly, the evaluation of our final radial velocity amplitude includes the information of many spectral lines that span the whole range in observation. The major uncertainty in our method is associated with the linear relationship between radial velocity amplitude and equivalent width. Due to the complicated physical processes involved in spectral line formation in a 3D atmosphere (e.g. Asplund et al. 2000), it is difficult to quantify higher order effects beyond the linear relation between $\mathfrak {v}$ and Wλ; that is, the systematic uncertainty of the linear fitting. Nevertheless, it is still illuminating to provide the statistical uncertainty. The statistical uncertainty is quantified using the bootstrap method. The data set considered here is the radial velocity amplitude and equivalent width of 49 fictitious Fe i lines. We conduct 10 000 bootstrap samplings, that is, generating 10 000 data sets each containing 49 randomly sampled $\mathfrak {v}$ and Wλ pairs. A linear regression between equivalent width and radial velocity is then performed for each re-sampled data set. For each fitting, we compute the equivalent width weighted mean radial velocity amplitude for all selected Fe i lines. The bootstrap method therefore results in 10 000 weighted mean radial velocity amplitudes, their mean and variance is the desired final radial velocity amplitude and its statistical uncertainty, which is 72.2 ± 0.5 m s−1 for the 3D solar model and 93.2 ± 0.3 m s−1 for the ϵ Tau model.
Citation: Asplund et al. 2000
Citation function: Uses; Compare/Contrast
The stellar metallicity in the Universe evolves with redshift (Mannucci et al. 2010; Sommariva et al. 2012; Krumholz & Dekel 2012; Dayal, Ferrara & Dunlop 2013; Madau & Dickinson 2014). The metallicity at a high redshift (z > 2) is much smaller in comparison to the low redshift Universe z  2. The first-generation stars contaminate the interstellar medium and cause a chemical evolution of the Universe. We can treat the metallicity evolution with redshift by a relation 
(2)$$\begin{eqnarray*}
\log _{10}(Z(z))= \gamma z +\zeta ,
\end{eqnarray*}$$where γ captures the redshift dependence and ζ captures the metallicity value at z = 0 (Mannucci et al. 2010; Madau & Dickinson 2014). This relation captures the metallicity of the parent star or the gas cloud from which a star has formed. It is written to express only a mean evolution of the metallicity. Along with the mean metallicity evolution of the Universe, there is going to be a scatter in the metallicity depending on the galaxy properties. Such a source of uncertainty brings additional stochasticity to the metallicity relation. Currently, a limited number of observations (Gallazzi et al. 2008; Mannucci et al. 2010; Krumholz & Dekel 2012) are available to explore the environment dependence of the metallicity, and most of our current understandings are based on simulations(Genel 2016; Torrey et al. 2019). These studies show that the overall median metallicity dependence of the galaxies at different redshifts can be explained by power form (Pei, Fall & Hauser 1999; Young & Fryer 2007; Torrey et al. 2019). Several studies of GW merger rates and mass distribution are performed (Belczynski et al. 2002; Dominik et al. 2012, 2015; Mapelli et al. 2017; Giacobbo, Mapelli & Spera 2018; Toffano et al. 2019; van Son et al. 2022) which are motivated by these studies and show that the black hole mass distribution can exhibit a redshift dependence. The existence of any stochasticity in the galaxy metallicity distribution will also influence the mass distribution but is currently not well known. However, as the relation given in equation (1) is in terms of the logarithm of metallicity, so the impact of fluctuation around the median value depending on the individual galaxy properties is going to be a small (logarithmic) change. As we are unable to measure the host of the BBH due to a large sky localization error of the BBH, we cannot directly associate the properties of galaxies with BBH source properties. So, we can only infer an ensemble average mass distribution from the GW data and the additional stochasticity (which will depend on the host properties) will appear as an additional uncertainty in the measurement of MPISN. As a result, we consider a median distribution of galaxy metallicity and the dependence of MPISN on it.
Citation: Genel 2016
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Citation: Gallazzi et al. 2008
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Although most galaxies have an implied SFR that scatters within a factor two around the MS, some do show a significantly higher SFR. Those objects also exhibit a higher gas content, shorter gas depletion times (e.g., Genzel et al. 2015; Schinnerer et al. 2016; Tacconi et al. 2013, 2018), and higher dust temperatures (e.g., Magnelli et al. 2014). Likewise, the stellar-light radial distribution is different in these two galaxy populations; while MS galaxies are closely approximated by exponential disks (e.g., Bremer et al. 2018), those above (and below) it exhibit a higher central mass concentration (e.g., Wuyts et al. 2011). Based on this dichotomy and the parametrization of the MS over cosmic time, a scenario has been proposed to explain the evolutionary path of galaxies along the MS. Since the normalization of the MS, the gas fraction of galaxies, and cosmic molecular gas density decrease from z ∼ 2.5 to 0 at a similar pace (e.g., Speagle et al. 2014; Decarli et al. 2016; Tacconi et al. 2018), it is thought that MS galaxies evolved through a steady mode of star formation, possibly regulated by the accretion of cool gas from the intergalactic medium (e.g., Dekel et al. 2009; Kereš et al. 2009; Davé et al. 2010; Hodge et al. 2012; Romano-Díaz et al. 2014, 2017; Feng et al. 2015; Anglés-Alcázar et al. 2017). From theoretical predictions, the scatter of the MS could thus be explained as the result of a fluctuating gas inflow rate that is different in each galaxy (e.g., Tacchella et al. 2016; Mitra et al. 2017). In this context, a galaxy enhances its SFR and moves towards the upper envelope of the MS due to gas compaction. As the gas is depleted, the SFR decreases and the galaxy falls below the MS. As long as a SFG is replenished with fresh gas within a timescale shorter than its depletion time, it will be confined within the MS (Tacchella et al. 2016). On the other hand, the enhanced star formation efficiency of galaxies above the MS has been linked to mergers (e.g., Walter et al. 2009; Narayanan et al. 2010; Hayward et al. 2011; Alaghband-Zadeh et al. 2012; Riechers et al. 2013, 2014) and instability episodes in gas-rich disks (particularly at high redshift; e.g., Davé et al. 2010; Hodge et al. 2012; Wang et al. 2019).
Citation: Tacconi et al.; 2018
Citation function: Background
Using the CASSIS2 software, we detected 8 lines of glycolaldehyde, 31 lines of the aGg′ conformer of ethylene glycol, and 26 lines of methyl formate (see Table 1). The glycolaldehyde and methyl formate transitions are taken from the JPL spectroscopic database (Pickett et al. 1998), while the ethylene glycol transitions are from the CDMS catalog (Müller et al. 2001, 2005). The predictions are based on experimental data from Butler et al. (2001), Widicus Weaver et al. (2005) and Carroll et al. (2010) for glycolaldehyde, Christen et al. (1995) and Christen & Müller (2003) for ethylene glycol, and Ilyushin et al. (2009) for methyl formate. The frequencies of five of the detected glycolaldehyde lines were directly measured in the laboratory (Butler et al. 2001). Some of the lines result from a blending of several transitions of the same species. The lines that are strongly blended with other species are not listed in Table 1. All three species are emitted very compactly at the position of the continuum peak (\hbox{$\alpha_{2000}=03^{\rm h}28^{\rm m}55\fs57$}α2000=03h28m55.s57, \hbox{$\delta_{2000}=31\degr14\arcmin37\farcs1$}δ2000 = 31°14′37 .̋ 1). The angular sizes obtained with a circular Gaussian fit in the (u, ν) plane vary from a point source to a maximum of 1″ depending on the transition. The line fluxes listed in Table 1 were measured at the continuum peak position with the CASSIS software using a Gaussian fitting method (Levenberg-Marquardt algorithm). The lines that are contaminated in the wings by other transitions are consequently fitted with a sum of Gaussians. We carefully checked that the derived full widths at half maximum (FWHM) are consistent with the other line measurements. The average FWHM is about 4.5 km s-1 at 317 GHz, and 5.0 km s-1 at 225 and 242 GHz. The widths of the methyl formate lines at 87 GHz are quite broad (~12 km s-1). It is consequently difficult to completely exclude an additional flux contribution from other species. The variation of FWHM with the frequency can be explained by the spectral resolution of the observations that decreases toward the lower frequencies.
Citation: Christen & Müller (2003)
Citation function: Uses; Uses
It has been generally believed (see Kormendy & Ho 2013; Kormendy 2016, for recent reviews) that, unlike massive galaxies at high redshifts (e.g., 





) whose evolution is driven by a major merger, low-redshift galaxies largely evolve through secular evolution (i.e., in a slow and gentle manner) driven by internal processes within galactic disks and/or by environmental effects such as harassment or nurturing (see Kormendy & Kennicutt 2004 for details; see also Cowie et al. 1996; Conselice et al. 2014 for the cosmic evolution). It has also been generally believed accordingly that most activity (namely active galactic nuclei—AGNs) of supermassive black holes (BHs) at the centers of galaxies at low redshifts (e.g., 





, Cisternas et al. 2011; and even up to 





, Kocevski et al. 2012) appear to be fueled by the random accretion of gas via internal, secular processes working close to the BH (say, within a few hundred parsec, Cisternas et al. 2013; also see Hopkins & Hernquist 2006). In these cases, there is no connection between AGN activity and major mergers of their host galaxies. This non-connection appears to be particularly true for low-z AGNs hosting intermediate-mass black holes (IMBHs):12

12
 Following Greene & Ho (2007) and Dong et al. (2012), hereinafter we refer to BHs with 





 





 at the centers of galaxies as “low-mass” or “intermediate-mass” BHs; accordingly, for the ease of narration wherever it is not ambiguous, hereinafter we refer to AGNs hosting low-mass BHs as low-mass AGNs or IMBH AGNs. Normally, we prefer “intermediate-mass BHs” to “low-mass BHs” because of the possible confusion of the latter with the stellar-mass BHs in low-mass X-ray binaries (LMXBs).
 according to the analysis of their images by the Hubble Space Telescope (HST), the majority of low-mass AGNs live in late-type disk galaxies without a classical bulge (Greene et al. 2008; Jiang et al. 2011). Indeed, the accretion rate for an IMBH is so tiny (0.05 





 yr−1 even if at the maximum Eddington accretion) that a steady supply of fuel, in the form of stellar mass loss from evolved stars or Bondi accretion of hot gas in the innermost regions, is available readily much more than required (Ho 2008). “In fact, the paradox for local BHs is not whether there is enough fuel to light them up. Rather, the puzzle is how to keep them so dim despite the ready abundance of in situ gas.” (Kormendy & Ho 2013; see also Ho 2008). That is, the real problem seems to be this (see Ho 2009): there must be some mechanism (yet to know) to hinder the innermost fueling process.13

13
 According to W.-M. Gu (2016, private communication), AGN outflows, which can be launched even at very low accretion rates (e.g., Wang et al. 2013; Gu 2015), provide such a mechanism.
Citation: Cisternas et al. 2013
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Various groups around the world have proposed different models to explain the formation of these two peculiar classes of SNe Ia. Sub-Chandrasekhar limiting-mass WDs were believed to be formed by merging two sub-Chandrasekhar mass WDs (double-degenerate scenario), leading to another sub-Chandrasekhar mass WD, exploding due to accretion of a helium layer (Hillebrandt & Niemeyer 2000; Pakmor et al. 2010). On the other hand, the super-Chandrasekhar WDs were often explained by incorporating different physics, such as a double-degenerate scenario (Hicken et al. 2007), presence of magnetic fields (Das & Mukhopadhyay 2013, 2014), presence of a differential rotation (Hachisu et al. 2012), presence of charge in the WDs (Liu et al. 2014), ungravity effect (Bertolami & Mariji 2016), lepton number violation in magnetized WD (Belyaev et al. 2015), generalized Heisenberg uncertainty principle (Ong 2018), and many more. However, none of these theories can self-consistently explain both of the peculiar classes of WDs. Moreover, each of these has some caveats or incompleteness, mostly based on the stability (Komatsu et al. 1989; Braithwaite 2009). Furthermore, numerical simulations showed that a merger of two massive WDs could never lead to a mass as high as 2.8M⊙ owing to the off-center ignition and formation of a neutron star rather than an (overluminous) SN Ia (Saio & Nomoto 2004; Martin et al. 2006). Hence, all the conventional pictures failed to explain the inferred masses of both the sub- and super-Chandrasekhar progenitor WDs and also both classes of progenitor WDs simultaneously by invoking the same physics. Moreover, each of the theories can explain only one regime of SN Ia, but it seems more likely that the nature would prefer only one scenario/physics to exhibit the same class of SNe. Whether it be an under- or overluminous SN Ia, other physics such as the presence of Si, etc., remains the same. Therefore, we seem to require just one theory to explain all the SNe Ia.
Citation: Liu et al. 2014
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