adsabs/FOCAL huggingface dataset:
When we now compare the photodesorption yields at 541 eV between fluences  5 × 1015 photon cm−2 and at 3 × 1017 photon cm−2 in Fig. 2, the CO2 photodesorption first increases from 2.6 × 10−2 molecule/photon to 7.3 × 10−2 molecule/photon. We also observed this phenomenon for CO photodesorption yield (the data are not shown for more clarity), which increased from 1.9 × 10−2 molecule/photon to 3.5 × 10−2 molecule/photon. Second, the estimated yield for the X-ray photodesorption of CH3OH from pure methanol ice decreased by almost one order of magnitude from 9.0 × 10−3 molecule/photon to 1.3 × 10−3 molecule/photon. This indi- cates that the photodesorption of CH3OH is higher for a lower fluence received by the ice when more intact methanol molecules are present in the ice. This aging process favors the photodesorption of simpler molecules such as CO2 or CO. Laffon et al. (2010) estimated with NEXAFS spectroscopy (at the C K-edge) that X-ray irradiation at 150 eV of pure methanol ice at 20 K leads to a survival rate of 50% for methanol after an absorbed dose of 1.1 MGy. In our fixed-energy experiments, we irradiated pure methanol ice with fluences between 5 × 1015 photon cm−2 and 2 × 1016 photon cm−2. Because we irradiated a volume of 0.1 cm2 × 100 ML, with a meanenergy of ~550 eV, and when we consider a volumic mass of condensed methanol of ~ 0.64 g cm−3 (at 20 K; Luna et al. 2018) and an X-ray absorption cross section of ~ 0.6 Mbarn (Ishii & Hitchcook 1988), the absorbed doses used in our fixed energy experiments change from ~ 2 to ~ 15 MGy, whichis quite similar to the absorbed doses in Laffon et al. (2010). This indicates that we could expect a methanol destruction rate of about 50% for our low-fluence experiments. In similar experiments, when irradiating a H2 O:CH4:NH3 (2:1:1) ice mixture covered by a layer of CO:CH3OH (3:1) with 250–1250 eV X-rays during 120 min with a flux of 7.6 × 1014 photon s−1, higher by almost two order of magnitudes than our experiments, Ciaravella et al. (2020) did not detect a desorption signal on the mass channel 31 (attributed to methanol desorption) and estimated that only ~ 20% of methanol molecules remained intact in the first minutes of the irradiation. The irradiation flux therefore appears to be critical for detecting methanol desorption in X-ray irradiation experiments of methanol-containing ices. A lower X-ray flux appears to favor methanol desorption because the methanol destruction rate is lower. This destruction of methanol molecules could also have a significant effect on the formation and desorption of more complex molecules.
Citation: Laffon et al. (2010)
Citation function: Similarities
Citation: Laffon et al. (2010)
Citation function: Uses
Given that the CDM paradigm only begins to face significant problems at precisely such scales when complex baryonic physics begins to play an important role, it is natural to inquire whether it is the central culprit behind erroneous theoretical predictions. It was for example realized early on that energy from supernovae may be sufficient for driving gas out of the potential wells of dwarf galaxies, the associated mass deficit resulting in the expansion of the central halo region and the flattening of the density profile. More generally, many hydrodynamical simulations implementing stellar and active galactic nuclei (AGN) baryonic feedback processes in a cosmological context are able to reproduce cores (e.g. Governato et al. 2010, 2012; Macciò et al. 2012b; Martizzi et al. 2012; Di Cintio et al. 2014; Chan et al. 2015). However, the complexity of such simulations obscures the physical mechanisms through which these processes affect the dark matter distribution. These mechanisms normally invoke ‘heating’ of the cold central density cusp through an irreversible process, such as dynamical friction from infalling clumps (El-Zant, Shlosman & Hoffman 2001; El-Zant et al. 2004; Tonini, Lapi & Salucci 2006; Romano-Díaz et al. 2008; Goerdt et al. 2010; Cole, Dehnen & Wilkinson 2011; Del Popolo et al. 2014; Nipoti & Binney 2015). Alternatively, repeated gravitational potential fluctuations induced by stellar winds, supernova explosions and AGN could also dynamically heat the central halo (Read & Gilmore 2005; Mashchenko, Couchman & Wadsley 2006; Mashchenko, Wadsley & Couchman 2008; Peirani, Kay & Silk 2008; Governato et al. 2012; Pontzen & Governato 2012, 2014; Zolotov et al. 2012; Martizzi, Teyssier & Moore 2013; Teyssier et al. 2013; Madau, Shen & Governato 2014; Ogiya & Mori 2014). Although the last mechanism may seem most closely related to the supernovae driven wind outflows discussed above, it is in principle more closely connected to the dynamical friction proposal, in the sense that it involves irreversible stochastic dynamics: one may envisage the potential fluctuations leading to cusp-core transformation as originating from stochastic density variations; the relevant ‘clumps’ would be associated with fluctuation scales, as opposed to physically distinct objects dissipating orbital energy via dynamical friction; nevertheless, the basic physical mechanism through which the energy is transferred to the dark matter is similar. For, as is the case in general with processes involving fluctuation and dissipation, fluctuations in a gravitational system can be approximated as stochastic processes described by power spectra and correlation functions, and they can be accompanied by dissipation in the form of dynamical friction (Chandrasekhar 1943; Nelson & Tremaine 1999).
Citation: Goerdt et al. 2010
Citation function: Background
In Figures 1 and 2 we show that the X-ray (1.7 keV5

5
This value corresponds to the geometric mean of the XRT energy band, at which the error of the estimated flux can be reasonably suppressed.
), optical (R-band), and radio (6 GHz) fluxes varied with the time of observation applied to the proper corrections if observed at a distance of 200 Mpc, motivated by the fact that the averaged sensitive range of the Advanced LIGO/Virgo detectors in their full-sensitivity run is about 210 Mpc, for the current samples. Due to the faintness of the SGRB afterglow emission, there are gaps of the data between the previous more distant events and GW170817/GRB 170817A (please note that for the latter we only consider the quick decline phase as the early part is significantly influenced by the beam effect of the off-axis outflow). Therefore we extrapolate the very late (t > 200 day) X-ray and optical afterglow data of GRB 170817A to t ∼ 2 day after the burst and then compare them to other events. The radio to X-ray spectrum of the forward shock afterglow emission of GW170817/GRB 170817A is fν ∝ ν−0.6, which yields a p = 2.2 in the slow-cooling synchrotron radiation scenario (Lamb et al. 2018; Troja et al. 2018). In the jet model, such a p can also reasonably account for the very late flux decline of 





 (Lamb et al. 2018). The extrapolation function of the forward shock emission of GRB 170817A to early times is thus taken as f ∝ t−2.2. Surprisingly, the forward shock afterglow emission of GW170817/GRB 170817A, the first neutron star merger event detected by Advanced LIGO/Virgo, are among the brightest ones for all SGRBs detected so far. Just a few events have X-ray afterglow emission brighter than that of GRB 170817A, as demonstrated in the right panels of Figure 1. The same conclusion holds for the optical and radio afterglow data, as shown in Figure 2, though these two samples are rather limited. We have also compared the distribution of the isotropic gamma-ray energy Eiso, calculated in the rest-frame energy band of 1–104 keV, for the SGRBs with well-measured spectra, and found no significant difference for the SGRBs with and without “long-lasting” afterglow emission (see Figure 3; where the number of events for the X-ray sample are smaller than that presented in Figure 1 because some bursts lack reliable spectral measurements). GRB170817A and GRB 150101B (Troja et al. 2018), two short events with the weakest detected prompt emission, have “bright” late-time afterglow emission because of their off-axis nature.
Citation: Lamb et al. 2018
Citation function: Uses
Citation: Lamb et al. 2018
Citation function: Uses
Oort Cloud. The conclusions that can be reached from this figure about the formation of the Oort Cloud are well known. For example, T93 showed that the parameter space in which an Oort Cloud forms is quite restricted, and that those Oort Clouds that do form have a narrow range of semimajor axes ∼10 000 au. While this parameter space is inhabited by Uranus and Neptune in the Solar system, which should thus readily supply objects to the Oort Cloud (even accounting for the possibility that these planets may have started closer to the Sun; Tsiganis et al. 2005), it could be that Oort Clouds are relatively rare. Many simulations have confirmed these predictions regarding the ability of planets to implant objects in the Oort Cloud (e.g. Dones et al. 2004) while also showing further subtleties such as the ability of Jupiter and Saturn to place a small fraction of the objects they scatter into the Oort Cloud even if ejection is the predominant outcome in such encounters (Brasser, Duncan & Levison 2008). The time-scale predicted for the scattering process to occur is also borne out in numerical simulations. For example, compare the prediction of Fig. 1 that it should take 0.1–1 Gyr for Uranus and Neptune to implant material in the Oort Cloud with fig. 13 of Dones et al. (2015). The radius at which the Oort Cloud forms in the simulations also agrees with that predicted of ∼10 000 au, with some studies includingthe differentiation between inner and outer Oort Clouds (e.g. Lewis, Quinn & Kaib 2013; Brasser & Schwamb 2015). Inspection of Fig. 1 shows that Oort Clouds could form at smaller orbital radii, but that such an outcome requires both low-mass planets and a large system age; for example, if Neptune and Uranus were each of Earth mass, then the Oort Cloud would be at ∼1000 au, but would take ∼20 Gyr to form. Another way to achieve a small Oort Cloud on a shorter time-scale is to place the planetary system in a dense stellar environment (as discussed in Section 3.2).
Citation: Tsiganis et al. 2005
Citation function: Compare/Contrast; Compare/Contrast
The first possible scenario is that all FRBs are generated from magnetars, the population of non-repeating FRBs are from magnetars born in the delay formation channels (Margalit et al. 2019) such as neutron star−neutron star mergers associated with short gamma-ray bursts (Rosswog et al. 2003; Price & Rosswog 2006; Giacomazzo & Perna 2013), neutron star−white dwarf mergers possibly associated with rapidly evolving supernovae (SNe) (Toonen et al. 2018; Zhong & Dai 2020), or white dwarf−white dwarf mergers (Yoon et al.2007; Schwab et al. 2016) possibly associated with SNe Ia, and accretion-induced collapse of white dwarfs (Nomoto & Kondo 1991; Tauris et al. 2013; Schwab et al. 2015); the population of repeating FRBs are from magnetars born in the prompt formation channels such as type I superluminous SNe, long gamma-ray bursts, or core-collapse SNe (Duncan & Thompson 1992; Kouveliotou et al. 1998; Kasen & Bildsten 2010). Nonetheless, it seems to be untrue based on recent observations in host galaxies and offsets of the repeaters FRBs 20180916B (Marcote et al. 2020; Tendulkar et al. 2021) and 20200120E (Bhardwaj et al. 2021a; Kirsten et al. 2021). So it may be that the observed non-repeating and repeating FRBs are respectively from less active and more active magnetars due to different magnetic field strengths. If this is the case, however, it is unclear whether the distribution discrepancies of properties between the one-off events and repeaters can arise from different magnetic field strengths. Moreover, if the one-off events and repeaters are produced by an identical mechanism like that generating FRB 20200428 and its associated X-ray burst, it is conceivable that the more active magnetars yielding repeaters could produce more frequent more violent outbreaks resulting in brighter bursts than the less active magnetars yielding one-off events. This appears to be inconsistent with the statistical results shown in Figure 3, though it is still a complicated issue.
Citation: Schwab et al. 2016
Citation function: Background; Background
It is important in our analysis to only keep sources for which the SFR and stellar mass estimates are reliable. To this end, we first excluded sources whose fit had reduced χ2, 




χ

red
2

>5

$ \chi ^2_{\rm red}>5 $



. This criterion is based on visual inspection of the SED fits and has been adopted in previous studies (e.g., Masoura et al. 2018; Mountrichas et al. 2019; Buat et al. 2021). Increasing the limit to 




χ

red
2

>6

$ \chi ^2_{\rm red}>6 $



 adds more sources to our sample, but most of them have bad fits and thus unreliable host galaxy measurements. Reducing the threshold to 




χ

red
2

>4

$ \chi ^2_{\rm red}>4 $



 would exclude additional sources, the vast majority of which have reliable fits. This criterion eliminates 133 AGN (9% of the sample). For each parameter calculated by the SED fitting process, X-CIGALE estimates two values. One is evaluated from the best-fit model, and one weights all models allowed by the parametric grid, with the best-fit model having the heaviest weight (Boquien et al. 2019). This weight is based on the likelihood, exp (−χ2/2), associated with each model. A large difference between these two values for a specific parameter indicates that the fitting process did not result in a reliable estimation for this parameter (Mountrichas et al. 2021a,c; Buat et al. 2021). Thus, when we compared the stellar masses of type 1 and 2 AGN, we only included in our analysis sources with 




1
5

≤


M


∗
,
best



M


∗
,
bayes



≤
5


$ \frac{1}{5}\leq \frac{M_{*,\rm best}}{M_{*, \rm bayes}} \leq 5 $


, where M∗,best and M∗,bayes are the best-fit and Bayesian-fit values of M*, respectively. This method to exclude unreliable estimations has been applied in recent studies (Mountrichas et al. 2021a,c; Buat et al. 2021). Using different values for the boundaries of the criterion (i.e. 0.1–0.33 for the lower limit and 3–10 for the upper limit) does not affect the results of our analysis. This criterion reduces the sample to 944 X-ray AGN (93% of the initial dataset). Of these systems, 729 are type 1 and 215 are type 2 (Table 2). Similarly, for the comparison of the SFR of the two AGN types, we require 




1
5

≤



SFR
best




SFR
bayes



≤
5


$ \frac{1}{5}\leq \frac{\mathrm{SFR_{best}}}{\mathrm{SFR_{bayes}}} \leq 5 $


, where SFRbest and SFRbayes are the best and Bayesian values of the SFR, respectively, estimated by X-CIGALE. This reduces our sample to 860 X-ray AGN (85% of the initial dataset). Of these sources, 673 are type 1 and 187 are type 2. We note that throughout our analysis we used the Bayes calculations of X-CIGALE for the various parameters.
Citation: Boquien et al. 2019
Citation function: Uses
Also plotted in Fig. 2 is the pressure in the outer crust from some popular EoSs that model the complete structure of the NS. The figure is drawn up to nb = 3  ×  10-4 fm-3, thus comprising the change from the outer crust to the inner crust in order to allow comparison of the EoSs also in this region (notice, however, that inner crust results are not available for the FRDM). We show in Fig. 2 the EoS from the recent BSk21 Skyrme nuclear effective force (Pearson et al. 2012; Fantina et al. 2013; Potekhin et al. 2013; Goriely et al. 2010) tabulated in (Potekhin et al. 2013). The parameters of this force were fitted (Goriely et al. 2010) to reproduce with high accuracy almost all known nuclear masses, and to various physical conditions including the neutron matter EoS from microscopic calculations. We see in Fig. 2 that after the experimentally constrained region, the BSk21 pressure is similar to the BCPM and FRDM results, with just some displacement around the densities where the composition changes from a nucleus to the next one. In the seminal work of BPS (Baym et al. 1971b) the nuclear masses for the outer crust were provided by an early semi-empirical mass table. The corresponding EoS is seen to display a similar pattern with the BCPM, FRDM, and BSk21 results in Fig. 2. The EoS by Lattimer & Swesty (1991), taken here in its Ska version (Lattimer 2015; LS-Ska), and the EoS by (Shen et al. 1998b,a; Sumiyoshi 2015; Shen-TM1) were computed with, respectively, the Skyrme force Ska and the relativistic mean-field model TM1. In the two cases the calculations of masses are of semiclassical type and A and Z vary in a continuous way. Therefore, these EoSs do not present jumps at the densities associated with a change of nucleus in the crust. Beyond this feature, the influence of shell effects in the EoS is rather moderate because to the extent that the pressure at the densities of interest is largely determined by the electrons, small changes of the atomic number Z compared with its semiclassical estimate modify only marginally the electron density and, consequently, the pressure. The LS-Ska EoS shows good agreement with the previously discussed models, with some departure from them in the transition to the inner crust. The largest discrepancies in Fig. 2 are observed with the Shen-TM1 EoS that in this region predicts a softer crustal pressure with density than the other models.
Citation: Baym et al. 1971b
Citation function: Background; Background; Similarities
We fitted the XMM-Newton spectra of ULX-1 and ULX-2 from observation 0794581201 with an absorbed power-law model, using two tbabs absorption components, one frozen to the Galactic value of NH = 1.84 × 1021 cm−2 and the other allowed to vary. In the case of ULX-1, a power-law model was not sufficient to produce a good fit, with χ2/dof = 131.9/90 and the fit showing significant soft residuals at ∼1 keV. These soft residuals are known to be a common feature in the spectra of ULXs and bright X-ray binaries (e.g., Bauer & Brandt 2004; Carpano et al. 2007; Middleton et al. 2015b), and found in other ULXs to be a combination of emission and absorption features related to powerful outflowing winds (including in NGC 6946 ULX-3; Pinto et al. 2016). We used an additional Gaussian component to empirically fit these soft residuals, which resulted in a very large statistical improvement and provided an acceptable fit (χ2/dof = 84.5/87). Using the best-fitting model for each source, we calculated the absorbed 0.3–10 keV flux of both objects. We do not correct for absorption since extending steep power laws, which are empirical rather than physical models, to low energies is likely to overestimate the true flux of the object beneath the absorption—unabsorbed fluxes using such models can be a factor of 2 or 3 higher than those for more physically motivated models, and the amount of absorption is itself model-dependent, so we make fewer assumptions by just considering the observed, absorbed flux. For comparison, we also fitted both spectra with an absorbed multicolor disk model. Although this was able to produce a statistically acceptable fit, the Gaussian representing the soft residuals in ULX-1 contributes to a physically unreasonable portion of the spectrum, broadening to fit the majority of the soft emission, and the residuals for ULX-2 are even less well characterized than for a power-law fit. Therefore, a steep power law seems to be the best empirical model for both spectra. We present the spectral fit results for these two sources in Table 3 and the best-fitting model plots in Figure 2.
Citation: Pinto et al. 2016
Citation function: Similarities; Similarities
The observations of the characteristic double-horned line profiles (Stewart et al. 2014) are all restricted to very low redshifts (z  0.5). Our understanding of the kinematics of H i at high redshifts (z > 0.5) majorly depends on the study of the QSO absorption spectra where the absorption features are mainly dominated by the Lyman−α systems (Zafar et al. 2013). The systems with the largest column densities, the DLAs, are proposed to be the progenitors of the present-day spiral galaxies (Wolfe et al. 2005). Modelling of DLAs observations suggests that DLAs resemble rotating disc galaxies with circular velocities typically of the order of $100\!-\!200\, {\rm km\, s}^{-1}$ (Wolfe et al. 1986; Kauffmann & Charlot 1994; Klypin et al. 1995; Lanzetta, Wolfe & Turnshek 1995; Wolfe 1995; Jedamzik & Prochaska 1998). A number of theoretical studies indicate that the DLAs can have circular velocities as low as ${\sim } 50 \, {\rm km\, s}^{-1}$ (e.g. Kauffmann 1996) which is supported by a number of numerical simulations (Pontzen et al. 2008; Cen 2012; Bird et al. 2014, 2015). QSO absorption spectra also suggest that the gas inside the DLAs has a velocity dispersion of $\sigma _v \approx 5\!-\!10\, {\rm km\, s}^{-1}$ (Wolfe et al. 2005) which is comparable with the nearby galaxies. Other observations also reveal that the high-redshift galaxies show rotational motion (e.g. see Pettini 2009 and references therein). Based on these considerations we have assumed that across the entire range z ≤ 6 the H i resides in rotating disc galaxies which exhibit a double-horned line profile similar to those seen for local galaxies. The value of the parameters α, hf, σv are found to vary from galaxy to galaxy in the local Universe. The statistics of these parameters and their redshift evolution are largely unknown. In our analysis each simulation corresponds to fixed values of these parameters which are held constant over the entire z range. We have carried out simulations covering the entire range of parameter values in order to estimate how this variation affects the RSD. Considering vcirc which determines the overall width of the line profile, it may be noted that the values are determined by the halo mass distribution which evolves with z.
Citation: Wolfe et al. 1986
Citation function: Background