adsabs/FOCAL huggingface dataset:
We adopted the model developed by Ďapek et al. (2005), in which 1D thermal conduction below each of the surface facets is solved numerically with the nonlinear Robin boundary condition at the surface, and the assumption of an isothermal core at a sufficient depth is made. A temperature-dependence of the thermal conductivity following Eq. (2) was used. For the sake of simplicity, the specific heat capacity c was assumed constant, c = 560 J kg−1 K−1, and the regolith grain density obtained for C-type meteorites was used, ρ = 3.11 g cm−3 (both from Gundlach & Blum 2013). We ran solutions for four values of the packing factor ϕ in the range between 0.3 and 0.6. Each time, the parameters of the thermal conductivity were adjusted to satisfy the constraints from thermal observations described in Sect. 3.3 (see Table 2). The time domain of one revolution about the Sun was divided into steps of 60 s, short enough when compared to the ≃3.6 h rotation period, and the space grid describing the depth below each of the surface increased exponentially, as described in Ďapek et al. (2005). We ensured that at each depth, the von Neumann stability condition was satisfied. Typically, ten iterative steps of the algorithm provide the temperature with an accuracy of one degree or better in the whole space and time domain of the solution. The shape and spin state of Phaethon was taken from the modeling in Sect. 3.1. Similarly, the volume-equivalent size of 5.1 km from Sect. 3.2 was used as an implicit value. The last parameter required to compute the thermal recoil acceleration (the Yarkovsky effect) is the bulk density of Phaethon. Our nominal models use 1 g cm−3 for the clarity, but we treated this value as a free parameter in the orbit determination process (similarly to what was done for asteroid Bennu in Chesley et al. 2014). Scaling to different densities is easily implemented by using the inverse-proportional dependence of the thermal acceleration on the bulk density. In our analysis we neglected the enhancement of the Yarkovsky effect that is due to surface roughness (Rozitis & Green 2012). This effect could cause an increase in our bulk density estimate of less than 10%, which is well within the formal uncertainty.
Citation: Gundlach & Blum 2013
Citation function: Uses
Cavagnolo et al. (2010) suggested that the jet kinetic power is able to inflate the X-ray cavities or bubbles in different systems, including giant elliptical galaxies and cD galaxies (Type cD galaxy, a subtype of type-D giant elliptical galaxy), and proposed to evaluate the kinetic power Pkin = Pcav. However, this method is only limited to a small number of sources at present. It is known that the luminosity of extended region of radio jet, which is believed to be less Doppler-boosted, is related to jet kinetic power (Rawlings & Saunders 1991; Willott et al. 1999; Cavagnolo et al. 2010; Meyer et al. 2011), $P_{\rm rad} = \eta \, L_{\rm 5GHz}^{\rm ext}{}^{\kappa }$. Though the factor κ and η are given in discrepancy in literature due to the different sizes and source types of sample (Cavagnolo et al. 2010; Meyer et al. 2011), the ${\rm log}\, L^{\rm ext}_{\rm 5GHz}$ scales with the ${\rm log}\, P_{\rm rad}$ in the logarithmic space. We collect the total radio flux density from literature (Taylor et al. 1996 at 5 GHz; Piner & Edwards 2014 at 8.4 GHz; and Lister et al. 2018 at 15 GHz), and we convert the data at other frequencies to 5 GHz by assuming that
(14)$$\begin{eqnarray*}
S_{\rm 5GHz}^{\rm core} = S_{\rm \nu }^{\rm core} \, \, {\rm and} \, \, S_{\rm 5GHz}^{\rm ext} = S_{\rm \nu }^{\rm ext} \left(\frac{\nu }{\rm 5 \, GHz} \right)^{\alpha _{\rm ext}},
\end{eqnarray*}$$where the total radio flux is the sum of the flux of core and the flux of the extended region, Stot = Score + Sext, the αext = 0.75 and αcore = 0 (Fan et al. 2011; Pei et al. 2016, 2019, 2020). Together with the radio-core dominance parameter at 5 GHz
(15)$$\begin{eqnarray*}
R = \left(\frac{S^{\rm core}}{S^{\rm ext}} \right) (1+z)^{\alpha _{\rm core}-\alpha _{\rm ext}},
\end{eqnarray*}$$that we collect from Pei et al. (2020), we obtain $S_{\rm 5GHz}^{\rm ext}$ and calculate ${\rm log}\, L^{\rm ext}_{\rm 5GHz}$. The correlations of ${\rm log}\, P_{\rm rad}$ and ${\rm log}\, L_{\rm 5GHz}^{\rm ext}$ against ${\rm log}\, \beta _{\rm app}^{\rm max}$ are illustrated in Fig. 6 and linear regression results are listed in Table 2. Positive correlations of ${\rm log}\, P_{\rm rad}\, versus \, {\rm log}\, \beta _{\rm app}^{\rm max}$ and ${\rm log}\, L_{\rm 5GHz}^{\rm ext}\, versus \, {\rm log}\, \beta _{\rm app}^{\rm max}$ are found for blazars. The positive correlation of ${\rm log}\, P_{\rm rad}\, versus \, {\rm log}\, \beta _{\rm app}^{\rm max}$ holds for both FSRQs and BL Lacs when we consider them independently, while the positive correlation of ${\rm log}\, L_{\rm 5GHz}^{\rm ext}\, versus \, {\rm log}\, \beta _{\rm app}^{\rm max}$ only hold for BL Lacs. It is found that the motion of jet knots is significantly correlated with jet radiation power for both FSRQs and BL Lacs, however, the motion of jet knots is correlated with the kinetic power only for BL Lacs.
Citation: Lister et al. 2018
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Filippenko & Chornock (2001) first presented the dynamical estimate of mass of the source to be around 7.4 ± 1.1 M⊙. Recently, Radhika & Nandi (2014) claimed that the mass of XTE J1859+226 is perhaps in between 6.58 and 8.84 M⊙ which is similar to the prediction of Shaposhnikov & Titarchuk (2009), although the lower mass limit is estimated as 5.4 M⊙ by Corral-Santana et al. (2011). However, we consider the typical mass of the source as 7 M⊙. The distance of this source is around d ∼ 11 kpc (Filippenko & Chornock 2001). Steiner et al. (2013) measured the spin as ak ∼ 0.4; however, Motta et al. (2014b) recently reported that the spin of the source is ak ∼ 0.34. Since the spin predictions are quite close, we use ak ∼ 0.4 for this analysis. We estimate the fluxes Fx (see Table 1) of LHS and HIMS of the 1999 outburst of the source (Radhika & Nandi 2014). The corresponding disc luminosities are calculated as $L_{\rm disc}^{{\rm LHS}}=8.26 \times 10^{37}\ {\rm erg\ s^{-1}}$ and $L_{\rm disc}^{{\rm HIMS}}=1.85 \times 10^{38}\ {\rm erg\ s^{-1}}$, respectively. Now, it is reasonable to assume the accretion efficiency for rotating BH as η = 0.3 which corresponds to the accretion rate of the inflowing matter as ${\dot{M}}_{{\rm acc}}^{{\rm LHS}} = 0.304 {\dot{M}}_{{\rm Edd}}$ in LHS and ${\dot{M}}_{{\rm acc}}^{{\rm HIMS}} = 0.680 {\dot{M}}_{{\rm Edd}}$ in HIMS. For LHS, we use $R_{\dot{m}}=9.83$ per cent following our theoretical estimate where xs = 64.6rg for ak = 0.4, ${\mathcal {E}}=0.001\,98$ and λ = 3.18. Incorporating these inputs in equation (15), we obtain the jet kinetic power as $L^{{\rm LHS}}_{{\rm jet}} = 2.52\times 10^{37}\ {\rm erg\ s^{-1}}$. The maximum mass outflow rate for HIMS corresponding to ak = 0.4 is obtained from Fig. 9 as $R^{\rm max}_{\dot{m}}=17.5$ per cent for ${\mathcal {E}}=0.005\,47$ and λ = 3.1, where the shock transition occurs at 21.9rg. Using these values in equation (15), we obtain the maximum jet kinetic power as $L^{{\rm HIMS}}_{{\rm jet}} = 1.08\times 10^{38}\ {\rm erg\ s^{-1}}$ which we regard to be associated with the HIMS of this source.
Citation: Filippenko & Chornock 2001
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Citation: Filippenko & Chornock (2001)
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Citation: Radhika & Nandi (2014)
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Citation: Radhika & Nandi 2014
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Cluster mergers can stir the intra-cluster medium (ICM) and lead to complex distributions of density and temperatures. The X-ray surface brightness traces regions of high electron densities ($\propto n_e^{2}$) and the SZ is sensitive to the pressure (∝neT) along the line of sight. An offset in the peaks of these signals can be used as an indicator of the density and temperature distribution in the disturbed ICM. There are examples of merging clusters that show presence of X-ray–SZ offsets, such as Abell 2146 (AMI Consortium et al. 2011) and Bullet cluster (Malu et al. 2010). Simulations have shown that the offset is sensitive to initial relative velocity of the merging clusters and the mass ratio (e.g. Molnar, Hearn & Stadel 2012; Zhang, Yu & Lu 2014). The cluster PLCK G200.9−28.2  discussed in this work has its X-ray peak and the Planck detection peak offset by 3.4 arcmin, which is the extreme in the Planck  sample (PC12). The Planck  SZ positions have mean and median errors of 1.5 and 1.3 arcmin, respectively (Planck Collaboration IV 2013). The X-ray peak is located at the northern sub-cluster [Fig. 1(a) and Fig. 6(a)] and the Planck  SZ position is separated from it by 3.4 arcmin (700 kpc) in the direction of the radio relic. Due to the presence of a shock at the relic, the region is expected to be overpressured and thus may result in shifting the peak of the SZ-signal. The offset in the direction of the relic indicates possible physical origin for the offset in addition to the position reconstruction uncertainty of Planck. Based on the results of simulations, the offset can be explained as a result of two comparable mass sub-clusters with mass ratio between 1 and 3 (Zhang et al. 2014). Deep optical observations tracing the galaxy distribution in this cluster will be useful to measure the mass ratios of the sub-cluster masses. Due to the large uncertainty in the Planck  position we cannot analyse the offsets for a statistical sample of merging clusters. However, the offset in the X-ray and SZ positions opens an additional probe for understanding the properties of merging galaxy clusters.
Citation: Malu et al. 2010
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Using ANTARES data from the end of 2007–2017, a search for upward-going muon neutrinos and antineutrinos in spatial and temporal coincidence with 784 GRBs has been performed. The numerical model NeuCosmA was used to estimate the expected neutrino flux from each burst individually, in the context of one-zone internal shock model. A novel aspect of the search here presented is the inclusion in the data analysis chain of the uncertainty that possible unknown parameters, related to the characteristic activity of the central engine, can introduce in the neutrino flux evaluation. This is crucial in order to correctly interpret the validity of model-dependent results, in terms of upper limits set by non-detections of neutrinos in coincidence with GRBs (Adrián-Martínez et al. 2013; Aartsen et al. 2017). These parameters have been identified in the bulk Lorentz factor, variability time-scale, and source redshift, all of which are affecting the so-called dissipation radius, where shell collisions are realized. Among these parameters, the former was shown to impact the most GRB-neutrino flux predictions. At the same time, it is also possible to marginalize the uncertainty related to it by assuming a correlation with the source isotropic gamma-ray luminosity (which is in turn a physical observable). This was realized by relying upon the observational correlation found by Lü et al. (2012). As a result of such procedure, the minimum variability time-scale was found to contribute more than redshift to the uncertainty on the neutrino flux predictions from GRBs. Indeed, when letting tv free to vary, the estimated uncertainty on the neutrino flux expected from the model is observed to span up to several orders of magnitude. As a consequence, the expected ν-fluxes are provided with an uncertainty band of ±2σ. Analogously to previous ANTARES searches (Adrián-Martínez et al. 2013, 2017b; Celli et al. 2017), MC simulations of the signal predicted by NeuCosmA were performed, while the respective background was estimated directly from off-source data collected by ANTARES. Only track-like events reconstructed within 10° in radius from the expected GRB position were selected and in temporal correlation with the prompt gamma-ray emission.
Citation: Adrián-Martínez et al. 2013; Adrián-Martínez et al. 2013
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In the 50 Myr after a star particle is formed, it can undergo supernova (SN) explosions. These are randomly drawn from a delay-time distribution (Kimm et al. 2015). We employ the mechanical feedback model described in Kimm et al. (2015, 2017) and Rosdahl et al. (2018) and the amount of momentum injected into the gas depends on the phase of the SN that is resolved by the simulation as to capture the final momentum of the snowplow phase. The equivalent of 1051 ergs is injected into the gas for each SN. The maximum momentum that we inject is boosted according to Geen et al. (2015) to account for unresolved H ii regions (Kimm et al. 2017). For each massive star that explodes, 20 per cent of the mass is recycled back into the gas. This gas is metal enriched assuming a metallicity of 0.075. Following Rosdahl et al. (2018), we have calibrated the SN feedback in order to reproduce the high-redshift stellar mass–halo mass relation from abundance matching (Behroozi, Wechsler & Conroy 2013). For our simulation, this requires assuming that the mean SN progenitor mass is 5 M⊙ which leads to 4× more SN on average compared to a standard Kroupa IMF (Kroupa 2001). While not ideal, this results in galaxies that fall nicely on the stellar mass–halo mass relation (see fig. 1 of Katz et al. 2018) and produces a UV luminosity function consistent with observations for a similar set of simulations at approximately the same resolution (Rosdahl et al. 2018). While we cannot be certain that even our calibrated simulations have the correct stellar mass–halo mass relation as we are using a high-redshift extrapolation and there is intrinsically a lot of scatter, we have made an effort to calibrate on one of the best available predictions as any significant offset from the stellar mass–halo mass relation may lead to large systematic offsets in the SFR-line luminosity relations. In summary, our star formation and stellar feedback models are based on the work of Rosdahl et al. (2018) which were chosen to reproduce both a reasonable reionization history and UV luminosity function.
Citation: Behroozi, Wechsler & Conroy 2013
Citation function: Uses
Aided by the tight localization constraints of the three-detector network and the proximity of the GW source, multiple independent surveys across the EM spectrum were launched in search of a counterpart beyond the sGRB (Abbott et al. 2017c). Such a counterpart, SSS17a (later IAU-designated AT 2017gfo), was first discovered in the optical less than 11 hours after merger, associated with the galaxy NGC 4993 (Coulter et al. 2017a, 2017b), a nearby early-type E/S0 galaxy (Lauberts 1982). Five other teams made independent detections of the same optical transient and host galaxy all within about one hour and reported their results within about five hours of one another (Allam et al. 2017; Arcavi et al. 2017a, 2017b; Lipunov 2017b; Tanvir & Levan 2017; Yang et al. 2017; Soares-Santos et al. 2017; Lipunov et al. 2017a). The same source was followed up and consistently localized at other wavelengths (e.g., Corsi et al. 2017; Deller et al. 2017a, 2017b, 2017c; Goldstein et al. 2017; Haggard et al. 2017a, 2017b; Mooley et al. 2017; Savchenko et al. 2017; Alexander et al. 2017; Haggard et al. 2017c; Goldstein et al. 2017; Savchenko et al. 2017). The source was reported to be offset from the center of the galaxy by a projected distance of about 10″ (e.g., Coulter et al. 2017a, 2017b; Haggard et al. 2017a, 2017b; Kasliwal et al. 2017; Yang et al. 2017; Yu et al. 2017). NGC 4993 has a Tully–Fisher distance of ∼40 Mpc (Freedman et al. 2001; NASA/IPAC Extragalactic Database164

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The NASA/IPAC Extragalactic Database (NED) is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
), which is consistent with the luminosity distance measurement from gravitational waves (





 Mpc). Using the Tully–Fisher distance, the ∼10″ offset corresponds to a physical offset of ≃2.0 kpc. This value is consistent with offset measurements of sGRBs in other galaxies, though below the median value of ∼3–4 kpc (Fong et al. 2010; Fong & Berger 2013; Berger 2014).
Citation: Yang et al. 2017
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Citation: Yang et al. 2017
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The observation of massive neutron stars Demorest et al. (2010), Antoniadis et al. (2013) indicates that the EoS of nuclear matter must be very stiff in the regime of high densities and low temperatures. The degree of stiffness in the nuclear matter EoS is directly related to the repulsive interaction among particles at high densities, as well as to the particle content in the core of the stars. In particular, it has been extensively discussed in the literature whether exotic degrees of freedom might populate the core of neutron stars. On the one hand, it is more energetically favorable for the system to populate new degrees of freedom, such as hyperons (Dexheimer & Schramm 2008; Ishizuka et al. 2008; Bednarek et al. 2012; Fukukawa et al. 2015; Gomes et al. 2015; Maslov et al. 2015; Oertel et al. 2015; Lonardoni et al. 2015, 2016); Biswal et al. 2016; Burgio & Zappalà 2016; Chatterjee & Vidana 2016; Mishra et al. 2016; Vidaña 2016; Yamamoto et al. 2016; Tolos et al. 2017); Torres et al. 2017), delta isobars (Fong et al. 2010;Schurhoff et al. 2010; Drago et al. 2014; 2016; Cai et al. 2015; Zhu et al. 2016), and meson condensates (Ellis et al. 1995; Menezes et al. 2005; Takahashi 2007; Ohnishi et al. 2009; Alford et al. 2010; Fernandez et al. 2010; Mesquita et al. 2010; Mishra et al. 2010; Lim et al. 2014; Muto et al. 2015), in order to lower its Fermi energy (starting at about two times the saturation density). On the other hand, the EoS softening due to the appearance of exotica might turn some nuclear models incompatible with observational data, in particular with the recently measured massive neutron stars. One possible way to overcome this puzzle is the introduction of an extra repulsion in the YY interaction Schaffner & Mishustin (1996), Bombaci (2016), allowing models with hyperons to be able to reproduce massive stars (Dexheimer & Schramm 2008; Bednarek et al. 2012; Weissenborn et al. 2012; Banik et al. 2014; Bhowmick et al. 2014; Gusakov et al. 2014; Lopes & Menezes 2014; van Dalen et al. 2014; Yamamoto et al. 2014; Gomes et al. 2015). Another possible solution is the introduction of a deconfinement phase transition at high densities Bombaci (2016), with a stiff EoS for quark matter, usually associated with quark vector interactions (see Klähn et al. 2013 and references therein).
Citation: Vidaña 2016
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Citation: Lonardoni et al. 2015
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The primary impact of the 1859 storm was on telegraphy (e.g. Boteler 2006). Today the principal ‘space weather’ threat is to the electric power grid (Baker et al. 2008; Hapgood, 2011, 2012; Oughton et al. 2016; Dyer et al. 2018). Because of this threat, several studies have been carried out to estimate how frequently such extreme space weather events may occur (e.g. Tsubouchi & Omura 2007; Riley 2012; Curto, Castell & Del Moral 2016; Riley & Love 2017). Such studies are dependent on the observations of a handful of storms that have approached or rivalled the Carrington geomagnetic storm in observed/inferred intensity and/or auroral extent. These include great storms in February 1872 (Chapman 1957a,b; Silverman 2008; Hayakawa et al. 2018c); September 1909 (Silverman 1995; Willis, Stephenson & Fang 2007; Love et al. 2019); May 1921 (Silverman & Cliver 2001; Kappenman 2006; Cliver & Dietrich 2013). The only such storm observed during the space era occurred in March 1989 (Allen et al. 1989; Silverman 2006a; Pulkkinen et al. 2012; Cid et al. 2014). More recently, in July 2012, a major backside eruption on the Sun was observed both remotely and in situ by the STEREO spacecraft (Kaiser et al. 2008; Russell et al. 2013; Liu et al. 2014; Riley et al. 2016). The interplanetary coronal mass ejection (CME) propagated to 1 AU in ∼20 h. Baker et al. (2013) calculated that had the eruption occurred on the frontside of the Sun – with optimal seasonal and local timing to maximize solar wind – magnetosphere coupling (worst case scenario (see e.g. Temerin & Li 2002) ─ it might have produced a storm greater than that inferred for the Carrington event. In addition to these storms, auroral evidence has recently been provided for two pre-1859 storms that may have ranked with the Carrington event: February 1730 (Hayakawa et al. 2018a), and, particularly, September 1770 (Willis et al. 1996; Nakazawa, Okada & Shiokawa 2004; Ebihara et al. 2017; Hayakawa et al. 2017e).
Citation: Kappenman 2006
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Irrespective of its origin, the [C II] emission is linked to the presence of stellar far-ultraviolet (FUV) photons (E 13.6 eV). As FUV photons are tied to the presence of massive O and B stars that have short lifetimes, the [C II] 158 μm line is also astar formation rate (SFR) indicator. Indeed, ISO, Herschel and SOFIA observations have demonstrated the good correlation between the [C II] luminosity and the SFR in the Milky Way and in regions of massive star formation in other galaxies (e.g., Kramer et al. 2013, 2020; Pineda et al. 2014, 2018; Herrera-Camus et al. 2015, 2018; De Looze et al. 2011). With ALMA and NOEMA, ground-based observations of the [C II] 158 μm line in high redshift galaxies have come into reach and such data are routinely used to infer SFRs (e.g., Walter et al. 2012; Venemans et al. 2012; Knudsen et al. 2016; Bischetti et al. 2018; Khusanova et al. 2021) based upon validations of this relationship in the nearby Universe (Herrera-Camus et al. 2018; De Looze et al. 2011). However, it is well-understood that the intensity of the [C II] line depends on the local physical conditions (Hollenbach & Tielens 1999). Observationally, the presence of the so-called [C II]-deficit – a decreased ratio of [C II] 158 μm luminosity to FIR dust continuum with an increasing dust color temperature and also with FIR luminosity – is well established (Malhotra et al. 2001; Díaz-Santos et al. 2013; Magdis et al. 2014; Smith et al. 2017). This deficit is particularly pronounced in (local) ultraluminous infrared galaxies (ULIRGs), very dusty galaxies characterized by vigorous embedded star formation (e.g., Luhman et al. 2003; Abel et al. 2009; Graciá-Carpio et al. 2011). This deficit, however, does not necessarily hold in the early Universe at high redshift (e.g., Stacey et al. 2010; Brisbin et al. 2015; Capak et al. 2015). Some studies have indicated that not only [C II] emission is deficient in some sources, but other FIR cooling lines ([O I], [O III], [N II]), as well (e.g., Graciá-Carpio et al. 2011; Herrera-Camus et al. 2018). These deficits must be linked to the global ISM properties and star-formation characteristics in these galaxies.
Citation: Herrera-Camus et al. 2018
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Citation: Herrera-Camus et al. 2018
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