adsabs/FOCAL huggingface dataset:
The collision velocity dependence of the coefficient of restitution between particles was observed in experiments (Bridges et al. 1996; Higa et al. 1996) and is discussed in the literature (e.g., Ramírez et al. 1999; Zhang & Vu-Quoc 2002). However, the experiments by Heißelmann et al. (2010), used in the present paper to support our assumption of a constant coefficient of restitution, do not see a variation of the coefficient of restitution between particles at low collision velocities (≤ 1 cm s−1). This discrepancy in results might originate in the nature of the collisions studied in these different experiments: Bridges et al. (1996) and Higa et al. (1996) performed collisions of a particle with a flat surface, while Heißelmann et al. (2010) observed particle-particle collisions in a free-floating environment. The latter is an experimental environment very similar to NanoRocks. In such inter-particle collisions in free-floating environments, other physical effects lead to a different behavior of the energy dissipation during collisions. In particular, the damping behavior of a large plate or surface is expected to differ from that of a same-sized particle, so that the velocity dependence of the coefficient of restitution might be an effect of the experimental setup in Bridges et al. (1996) and Higa et al. (1996). Colwell et al. (2016) and Brisset et al. (2018) studied collisions between a round cm-sized particle and a flat surface of fine grains. They also observed an increase of the coefficient of restitution with decreasing collision velocity. While the composition of the target surface was different than in Bridges et al. (1996) and Higa et al. (1996) (fine granular material vs. solid ice), the similar behavior of the coefficient of restitution supports the fact that particle-surface collisions are very different from particle-particle collisions, and coefficients of restitution are only velocity dependent for collisions with particles with very different sizes (a much larger particle can be approximated as a target surface).
Citation: Bridges et al. (1996)
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Citation: Bridges et al. (1996)
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Citation: Bridges et al. 1996
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Citation: Bridges et al. (1996)
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H2O ice on Pluto has long escaped spectroscopic detection, and based on initial New Horizons data appears to be exposed only in a number of specific locations, usually associated with red color, suggestive of water ice/tholin mix (Grundy et al. 2015; Cook et al. 2015). Nonetheless, water ice is likely to be ubiquitous in Pluto’s near subsurface, given its cosmogonical abundance, Pluto’s density, and its presence on Charon’s surface6. Absorption coefficients for pure water ice (kH2O) at sub-mm-to-cm wavelengths are discussed extensively by Mätzler (1998), who also provides several analytic formulations to estimate them as a function of frequency and temperature along with illustrative plots. We use the Mishima et al. (1983) formulation (see Appendix of Mätzler 1998). Its applicability is normally restricted to temperatures above 100 K, but Fig. 2 of Mätzler (1998) indicates the trend with temperature. Absorption coefficients extrapolated to 50 K (estimated as half the values at 100 K) are shown in Fig. 5. At 500 μm, our best estimate is kH2O = 0.25 cm-1, comparable to the above values for CH4 and N2 ices. The corresponding penetration length is therefore comparable to the diurnal skin depth but remains negligible compared to the seasonal skin depth, even for seasonal Γ = 25 MKS. According to these calculations, the seasonal layer would be probed only at a wavelength of ~4 mm and beyond. We also remark that the expression from Mishima et al. (1983) would give a penetration depth of 56 m at 2.2 cm, which is an order of magnitude larger than indicated by the laboratory measurements of Paillou et al. (2008). In addition, small concentrations of impurities can dramatically reduce the microwave transparency of water ice (e.g., Chyba et al. 1998 and references therein). Therefore, the above calculations likely indicate upper limits to the actual penetration depth of radiation in a H2O ice layer, from which we conclude that the seasonal layer is not reached at the Herschel wavelengths.
Citation: Mätzler 1998
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Citation: Mätzler (1998)
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Unfortunately, since the study by Onions et al. only used a single dark matter halo, albeit at exquisite numerical resolution, the comparison is limited to the relatively low-mass end of the subhalo mass function (SHMF), where the cumulative mass function N( > m), exceeds unity. In order to study the massive end of the SHMF, where N(>m)  1, one needs to average over large numbers of host haloes. The abundances of these rare but massive subhaloes has important implications for, among others, the statistics of massive satellite galaxies (e.g. Boylan-Kolchin et al. 2010; Busha et al. 2011) and the detection rate of dark matter substructure via gravitational lensing (e.g. Vegetti et al. 2010, 2012). In this paper, the second in a series, we use subhalo mass functions (SHMFs) and subhalo catalogues from a variety of numerical simulations that are publicly available, and that have been obtained using different subhalo finders, to compare SHMFs, focusing on the massive end. We confirm the findings by Onions et al., that the SHMFs are consistent at the 20 per cent level at the low-mass end. At the massive end, though, different subhalo finders yield subhalo abundances that differ by more than one order of magnitude! By comparing the simulation results with a new, semi-analytical model (Jiang & van den Bosch 2016, hereafter Paper I), we demonstrate that subhalo finders that identify subhaloes based purely on density in configuration space, such as the popular subfind and bdm, dramatically underpredict the masses, but not the maximum circular velocities, of massive subhaloes. We also show that the model predictions are in excellent agreement with the simulation results when they are analysed using more advanced subhalo finders that use phase-space and/or time domain information in the identification of subhaloes. We discuss a number of implications of our findings, in particular with regard to the power-law slope of the subhalo mass and velocity functions.
Citation: Busha et al. 2011
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Given the number densities within the mass-dissociation index plane of Fig. 8, we now ask ourselves whether known dissociated clusters, such as the Bullet cluster, are expected in L210N1024NR? The Bullet Cluster has a mass of $\sim 1.5 \times 10^{15} \, {\rm M}_{\odot }$ (e.g. Clowe et al. 2004; Bradač et al. 2006; Clowe et al. 2006) and we estimated a dissociation index of SBullet ∼ 0.335 ± 0.06. As seen in Fig. 8 there are no Bullet cluster analogues (structures of approximate mass and dissociation) in L210N1024NR, this is unsurprising as a simulation requires a significantly larger volume than that of L210N1024NR ((210cMpc h−1)3) to expect such an object (e.g. Lee & Komatsu 2010; Thompson & Nagamine 2012; Bouillot et al. 2015; Kraljic & Sarkar 2015; Thompson et al. 2015). From the distribution presented in Fig. 8, it is trivial to estimate the required cosmological volume (the effective volume, Veff) to expect structures of a given mass and dissociation index. By separating the 2D distribution on the mass-dissociation index planes into the component 1D distributions of mass and dissociation the effective volume is computed as
(12)$$\begin{eqnarray}
V_\text{eff}~^{-1} &amp;=&amp;\int \int \,{\rm{ d}} S \, {\rm{ d}} M \phi (S, M) \\
&amp;=&amp; \int _{S_\text{a}}^{S_\text{b}} \, {\rm{ d}} S \phi _S(S) \int _{M_\text{a}}^{M_\text{b}} \, {\rm{ d}} M \phi _M(M)~,
\end{eqnarray}$$where ϕS(S) is the number density function associated with S and $\phi _\mathit {M}(\mathit {M})$ is the mass function presented in Fig. 7. Assuming a probable range of S = 0.335 ± 0.06 and $1 \lt M \lt 2 \times 10^{15} \, {\rm M}_{\odot }$ we estimate a number density ∼4.92 × 10−10 Mpc−3 or that an effective volume of ∼2.03 Gpc3 would be required to observe a single Bullet-like cluster. This result is inline with the number density estimate of the order of ∼10−10 Mpc−3 by Thompson et al. (2015), which improves on previous estimates (e.g. Lee & Komatsu 2010; Thompson & Nagamine 2012; Bouillot et al. 2015) due to more sophisticated halo finding methods (e.g. Behroozi, Wechsler & Wu 2013). Conversely, it was estimated by Kraljic & Sarkar (2015) (utilizing the same halo finder as Thompson et al. 2015) that given an effective volume of ∼14.6 Gpc3, no Bullet cluster analogues are expected, however as indicated by a pairwise velocity distribution it would be expected that present binary halo–halo orbits have the potential to form a Bullet-like object.
Citation: Thompson et al. 2015
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Citation: Thompson et al. (2015)
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Citation: Thompson et al. 2015
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To better visualize the jet behaviour in this phase, we show in Fig. 10 the radio light curve at the core location for the first 25 d of the outburst, taken from Fig. 2. The compact jet emission peaks on MJD 58520 and then starts to decay on MJD 58521, 1 d before the system enters in the soft state and 2 d after the inferred RK1 ejection date (see Section 3.3). This whole evolution is accompanied by a smooth transition between the optically thick and the optically thin regimes of synchrotron radio emission. Compact jets are usually observed to quench at the transition from the hard to soft state (e.g. Fender et al. 1999; Corbel et al. 2000). This phenomenon is observed to start at higher frequencies (where the emission is produced closer to the compact object) and terminates as the jet break evolves through the radio band (Russell et al. 2013b, 2014; Russell et al. 2020b). It is not yet clear if compact jets switch off before or during the launch of discrete ejections, and how (or if) the two events are linked (Russell et al. 2020a). We do not have a MeerKAT observation on MJD 58521, but, as the flat spectrum obtained with ATCA suggests, the flux density would have been at the ∼50 mJy level. The following observations show instead a quick rise in flux density and a subsequent decay until MJD 58531, when the source is steadily in the soft state. With our data, we cannot conclude on the origin of the radio emission on MJD 58521, which could be produced either from the compact jet that is quenching (with no evidence of the spectral break in the radio band), or by the first self-absorbed part of the radio flare observed to peak on MJD 58523. The latter scenario implies that the jet significantly quenched in less than 2 d, a shorter time-scale with respect to what observed for other sources (e.g. Russell et al. 2013b, 2020a). Therefore, we are not able to precisely order in time the ejection of RK1 and the quenching of the compact jets, and thus we cannot draw conclusions on a potential link between the two events.
Citation: Russell et al. 2020b
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Long-duration gamma-ray bursts (GRBs) give rise to a synchrotron afterglow, detectable at optical wavelengths if sufficiently rapid and deep follow-up observations are made. A substantial fraction, however, lack such emission even when it would be expected from extrapolation of the X-ray spectral slope (Groot et al. 1998; Fynbo et al. 2001). When the X-ray to optical spectral slope, βOX, is below the recognized threshold of 0.5, the event is classified as ‘dark’ (Jakobsson et al. 2004). This is typically evaluated at 11 h post-burst to avoid contamination from early-time effects including X-ray flares and plateaus. An alternative method uses βOX  βX − 0.5 to define darkness (van der Horst et al. 2009). There are two primary causes for darkness in GRBs: attenuation by dust, or rest frame ultraviolet H i absorption at high redshift (e.g. Fruchter 1999; Levan et al. 2006; Perley et al. 2009,2013; Greiner et al. 2011; Svensson et al. 2012; Zauderer et al. 2013; Chrimes et al. 2019; Higgins et al. 2019). The number of GRBs known at high-redshift (z > 5, in the epoch of reionization) is small (∼15, from around 500 GRBs with a known or estimated redshift, Cenko et al. 2006; Grazian et al. 2006; Jakobsson et al. 2006; Kawai et al. 2006; Ruiz-Velasco et al. 2007; Salvaterra et al. 2009; Greiner et al. 2009; Tanvir et al. 2009; Cucchiara et al. 2011; Afonso et al. 2011; Castro-Tirado et al. 2013; Laskar et al. 2014; Jeong et al. 2014b; Chornock, Fox & Berger 2014b; Tanvir et al. 2018), and each one is valuable, as they provide insight into star formation in the low mass, low luminosity galaxies which power the epoch of reionization. Because they have small projected offsets from their hosts, high-redshift GRBs with a detected afterglow uniquely allow us to place accurate, deep upper limits on the luminosities of the faintest, undetected galaxies, probing fainter galaxies than deep field studies (Berger et al. 2007; Tanvir et al. 2012; Trenti et al. 2012; McGuire et al. 2016). For those with the brightest afterglows, insight into the burst environment can be gained from absorption lines in their spectra (e.g. Kawai et al. 2006; Chornock et al. 2014a; Sparre et al. 2014; Hartoog et al. 2015).
Citation: Svensson et al. 2012
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After the discovery of hard X-ray lag relative to soft X-rays in binary system (e.g. Miyamoto et al. 1988; Nowak et al. 1999), similar lags were also observed in AGN (Papadakis, Nandra & Kazanas 2001; McHardy et al. 2004). The origin of hard lag is not clearly known. One possible explanation is provided by the propagation fluctuation model in which fluctuations associated with accretion flow propagate inwards in an accretion disc and thus resulting in the emission of the soft photons from relatively outer regions earlier than the hard photons from the innermost regions (Lyubarskii 1997; Kotov, Churazov & Gilfanov 2001; Arévalo & Uttley 2006). Recently, for example, Swift monitoring of the radio-loud NLS1 galaxy PKS 0558−504 for ${\sim } 1.5{\rm \ {\rm yr}}$ has revealed that optical leads UV and UV leads soft X-rays on short time-scales of about a week (Gliozzi et al. 2013) possibly favouring the propagation model. A new type of lag has emerged from recent studies where soft photons lag to the hard photons. This is termed as the reverberation lag which is used to constrain the X-ray emitting region in AGN. Fabian et al. (2009) discovered the reverberation lag ∼ 30 s for the first time in a NLS1 galaxy 1H0707−495. Since then, such lags have been observed in dozen of Seyfert galaxies (Zoghbi et al. 2010; de Marco et al. 2011; Emmanoulopoulos, McHardy & Papadakis 2011b; Zoghbi & Fabian 2011; Zoghbi, Uttley & Fabian 2011; Cackett et al. 2013; De Marco et al. 2013; Fabian et al. 2013; Kara et al. 2013). The most of above cases reveal reverberation lag ∼100 s supporting the compact nature of X-ray-emitting region within few gravitational radii of a supermassive black hole (SMBH). In case of strong illumination, such as that implied by observation of strong blurred reflection, UV/optical emission from AGN may be dominated by the reprocessed emission and the variations in the optical/UV band emission lag behind the X-rays (e.g. McHardy et al. 2014). About a five year long campaign of Seyfert 1 galaxy Mrk 79 using six ground-based observatories for optical and RXTE for X-ray observations, Breedt et al. (2009) have shown zero lag between optical and X-rays on time-scale of about a day. Their study of correlated X-ray and optical emission suggests X-ray reprocessing on short time-scale of days and the changes in the optical emission on long time-scale of ∼ few years can be attributed to the variations in the accretion rate.
Citation: Kotov, Churazov & Gilfanov 2001
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The spatial distribution of OB stars and associations, young long-period Cepheids and open clusters, star-forming regions, H ii regions, interstellar dust, and giant molecular and neutral gas clouds in the solar vicinity that have been in existence generally τ ≲ 108 yr is known to correlate with the location of the inner Sagittarius, the closest Orion, and outer Perseus spiral arm segments. (The distances for the vast majority of these spiral tracers have been determined in the literature with trigonometric or photometric methods.) The Sun is situated at the inner edge of the Orion arm (Levine et al. 2006; Hou & Han 2014; Nakanishi & Sofue 2016; Xu et al. 2018, 2021; Lallement et al. 2019; Reid et al. 2019; Skowron et al. 2019; Cantat-Gaudin et al. 2020; Fig. 2 above).3 These three spatial features nearby to the Sun appear to form part of the global spiral structure in the Galaxy. Contrary, the objects of older population with larger random velocities, for instance, main-sequence A–K stars or the oldest Cepheids and open clusters, do not currently follow the exact location of those arms (e.g. Cantat-Gaudin et al. 2020, fig. 8 therein; Griv et al. 2020, fig. 7 therein). The latter can be explained by the difference in rotation velocity between the spiral density waves and the objects. Investigating the velocity field of Xu et al.’s (2018) O and early B-type stars in the framework of the Lin–Shu density-wave proposal, we also found that the Sun lies within the Orion arm, at the inner edge of this spiral. The radial distance from the Sun to the centre of the Orion arm is ≈0.2 kpc in the direction of the Galactic anticentre, the centre of the Sagittarius arm is ≈1.8 kpc from the Sun in the direction of the GC, and the width of the arms is ≈0.5 kpc. The radial distance between the centres of the Orion and Sagittarius arms near the Sun is λrad ≈ 2 kpc (cf. Hou & Han 2014; Wu et al. 2014; Bovy et al. 2015). As for us, the nearest Orion spiral arm forms part of the dominant density-wave structure of the system.
Citation: Cantat-Gaudin et al. 2020
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Citation: Hou & Han 2014
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Citation: Cantat-Gaudin et al. 2020
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Citation: Hou & Han 2014
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The core/cusp problem is a clear example of this controversy: on the one hand, cosmological dark matter only N-body simulations predict cuspy dark halo density profiles; on the other hand, the rotation curves of low surface brightness disc and gas-rich dwarf galaxies favour shallower or cored dark matter density distributions (de Blok 2010 and references therein). Also for dSphs, for which the determination of the dark matter density distribution is more difficult, there are indications that cored dark matter density profiles may be favoured with respect to cuspy profiles (Kleyna et al. 2003; Goerdt et al. 2006, Battaglia et al. 2008; Walker & Peñarrubia 2011; Salucci et al. 2012; Amorisco, Agnello & Evans 2013; Zhu et al. 2016), though this finding is still debated (Richardson & Fairbairn 2014; Strigari, Frenk & White 2017). It must be stressed, however, that cored dark haloes in dSphs do not necessarily imply a failure of ΛCDM: dark matter only cosmological simulations may not reliably predict the present-day dark matter distribution in dSphs because, by definition, they neglect the effects of baryons on the dark haloes. Even in a galaxy that is everywhere dark matter dominated today, baryons must have been locally dominant in the past to permit star formation. Therefore, the effect of baryon physics on the dark halo is expected to be important also in dSphs. For instance, Nipoti & Binney (2015) showed how, due to the fragmentation of a disc in cuspy dark halo, dynamical friction may cause the halo to flatten the original cusp into a core even before the formation of the first stars (see also El-Zant, Shlosman & Hoffman 2001; Mo & Mao 2004; Goerdt et al. 2010; Cole, Dehnen & Wilkinson 2011; Arca-Sedda & Capuzzo-Dolcetta 2017). Moreover, the results of hydrodynamical simulations suggest that, following star formation, supernova feedback can also help to flatten the central dark matter distribution, by expelling the gas (Navarro, Eke & Frenk 1996a; Read & Gilmore 2005) and thus inducing rapid fluctuations in the gravitational potential (Mashchenko, Couchman & Wadsley 2006, Pontzen & Governato 2012, Tollet et al. 2016).
Citation: Richardson & Fairbairn 2014
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We obtain computationally credible samplings of the posterior probability (equation 8) by removing the burn-in steps of the random walk according to the autocorrelation time. We can then create synthetic data sets by drawing a parameter sample $\pmb {\theta }_k$ from the posterior and using it to draw from the likelihood to create a new data set, i.e. drawing new σDj from the probability distribution for all galaxies in the original data set using equation (6). We then assess the validity of the model by comparing synthetic data with the observed (i.e. original) data. This comparison is done by using a discrepancy measure $\mathcal {D}(\sigma _\mathrm{ D}|\pmb {\theta }_k)$ between data and model-derived expected values for the same data $e=\lbrace e_j(\pmb {\theta }_k)\rbrace$, where $\boldsymbol{\theta}_k$ is drawn from the posterior distribution and σD can be the observed errors or the model-generated synthetic errors. The discrepancy can be calculated using a statistic like χ2 (de la Horra 2008; de la Horra & Teresa Rodriguez-Bernal 2012), but here we will work with the Freeman–Tukey discrepancy since it is weight independent (Brooks, Catchpole & Morgan 2000; Bishop, Fienberg & Holland 2007),

\begin{eqnarray*}
\mathcal {D}(\sigma _\mathrm{ D}|\pmb {\theta }_k)=\sum _j^m \left(\sqrt{\sigma _{\mathrm{ D}j}\vphantom{e_j(\pmb {\theta }_k)}}-\sqrt{e_j(\pmb {\theta }_k)}\right)^2.
\end{eqnarray*}
For each parameter draw k, it is possible to compare the simulated discrepancy with the observed discrepancy. If the model is representative of the data, then for many parameter draws, the simulated and observed discrepancies should be similar. We can then calculate a Bayesian ‘p-value’ as the ratio of ‘draws when the observed discrepancies are larger than the synthetic discrepancies’ to ‘total draws’. If this Bayesian p-value is too close to 0 or to 1 we can reject the model, otherwise it is generating synthetic data that are similar to the original data. This is better visualized using a discrepancy plot, where for each draw k, a synthetic discrepancy is paired with its corresponding observed discrepancy. If the discrepancy points are roughly equally distributed about the $\mathcal {D}_\mathrm{obs}=\mathcal {D}_\mathrm{sim}$ line, then we cannot reject the model. As mentioned above, we expect that galaxies with the largest number of measurements are sampling more completely the ‘true’ distribution of the distance. Therefore, we need to find the minimum number of measurements per galaxy for which the Bayesian p-value shows an agreement between on the partitioned data set and the model predictions.
Citation: de la Horra & Teresa Rodriguez-Bernal 2012
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