adsabs/FOCAL huggingface dataset:
There are several assumptions related to the FB origin. One of the first intriguing assumptions of the FB origin was the scenario of the evolution of the relativistic jet remnant. Presumably, relativistic jets existed during the recent activities of Sgr A* about 107 yr ago. These assumptions lead to many interesting discussions about FB’s inheritance from relativistic jets (Guo et al. 2012; Yang et al. 2012; Zhang & Guo 2020). The FBs are presumably filled with matter from jets originating from the supermassive black hole (SMBH) vicinity. The expansion process could occur at the front of a shock wave that has collided with the external environment and formed the boundaries of the currently observed FBs. On the other hand, Su & Finkbeiner (2012) provided the evidence of the possibility of a gamma jet existence, which could also lead to the filling of such a structure as FBs by hot particles. There is another possibility of the FB formation due to the tidal disruption of stars (Cheng et al. 2011; Chernyshov et al. 2014, 2017) and the acceleration of individual particles by the arising shock waves. The FB primary structure originating from supernova explosions in the central molecular zone (CMZ) is also possible (Lacki 2014). The young stars’ formation and the stellar winds may also have a role in the FB origin, including the CMZ life cycle (Armillotta et al. 2019). Cheng et al. (2011) also described a scenario of FBs’ periodic feeding by supernova explosions. Various interesting scenarios for the FB formation were also discussed by Mertsch & Petrosian (2019). Comparative analysis of the possible scenarios of FB origin, in general, has been of great interest for explaining its nature since discovery in 2010. It is also interesting to note that all scenarios that are mentioned above may complement each other. FBs may have really formed from relativistic jets and energized by intensive periodic (on average, once every 104 yr) processes within. Stellar plasma injected by supernova shock waves may be one of the important additional channels for the supply of energy to a stationary bubble formed from a jet.
Citation: Chernyshov et al.; 2017
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Tidal heating of Io has been shown to be responsible for its widespread volcanism. The tidal heating rate of Jupiter’s tidally locked moon, 





, driven by forced eccentricities, e, locked by Europa and Ganymede’s Laplace resonance with Io, is the dominant interior heating source. Similarly, the tidal heating of an exomoon will likely dominate the interior energy budget due to the additional stellar tide. Consequently, the tidal heating rate is orders of magnitude higher than at Io, which for an exo-Io of similar rheological properties (





, Rs = RIo, ρs = ρIo) can be written as (Cassidy et al. 2009; Equations (19) and (20))
3





where υ = 3 × 10−7 cm3 erg−1, and τs = τp/5 based on the tidal stability criterion discussed in Section 2. For utility, we describe the exo-Io’s tidal efficiency as 





, which can readily be computed for any three-body system as tabulated in Table 4. The enhanced tidal heating described in Equation (3) will also contribute to the surface temperature T0 = Teq + ΔT0, which is very roughly approximated as
4





where σsb is the Stefan–Boltzmann constant and Teq. At Io, the total neutral volcanic content (SO2, SO, NaCl, KCl, Cl, and dissociation products) ejected to space (Section 4.2.1) by the incident plasma is estimated to be, on average, ∼1000 kg s−1 (e.g., Thomas et al. 2004), varying within an order of magnitude over decades of observations (Burger et al. 2001; Wilson et al. 2002; Thomas et al. 2004). While the source of the dominant gas SO2 is ultimately tidally driven volcanism, the near-surface atmosphere is mostly dominated by the sublimation of SO2 frost (Tsang et al. 2016). By observing the atmospheric evolution of the SO2 column density with heliocentric distance, Tsang et al. (2013) estimated the direct volcanic component to be Nvolc ∼ 6.5 × 1016 cm−2, typically 





 of the total observed SO2 column density. Ingersoll (1989) demonstrated the relative contributions due to both sublimation and volcanic sources in maintaining Io’s atmosphere and established a relationship relating the volcanic source rate to the volcanically supplied atmospheric pressure:
5





This expression also gives the volcanic column density 





, where g is the acceleration due to gravity. Adopting an observed atmospheric temperature of Tatm = 170 K by Lellouch et al. (2015) corresponding to an atmospheric scale height of H = 12 km, a thermal velocity 





 equal to 150 m s−1, and a sticking coefficient α = 0.5 for the SO2 mass of 64 amu yields a volcanic source rate of 





 ∼ 6.9 × 106 kg s−1 of SO2 integrated over Io’s mass MIo. The average volumetric mixing ratio for NaCl to SO2 at Io is observed to be XNaCl ∼ 3 × 10−3 (Lellouch et al. 2003). This leads to a source rate of 





 ∼ 7.4 × 103 kg s−1 of NaCl, somewhat larger than but reasonably consistent with the direct measurement of the NaCl volcanic source rate of (0.8–3.1) × 103 kg s−1 (Lellouch et al. 2003). From these estimates, we will adopt ∼3 × 103 kg s−1 of Na i as the volcanic source rate for Io.
Citation: Thomas et al. 2004
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Citation: Thomas et al. 2004
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Observations have shown that the shape of the Lyα line is diverse. It includes broad damped absorption profiles, P-Cygni profiles, double-peak profiles, pure symmetric emission profiles, and combinations thereof (Kunth et al. 1998; Mas-Hesse et al. 2003; Shapley et al. 2003; Møller et al. 2004; Noll et al. 2004; Tapken et al. 2004; Venemans et al. 2005; Wilman et al. 2005). This variety can be understood through a detailed radiative transfer calculation, which is analytically solvable only for simple cases (e.g., a static, plane-parallel slab by Harrington 1973 and Neufeld 1990, and a static uniform sphere by Dijkstra et al. 2006). Later, numerical algorithms based on Monte Carlo techniques were developed to solve radiative transfer for more general cases. Now theoretical studies mostly rely on them (e.g., Spaans 1996; Loeb & Rybicki 1999; Ahn et al. 2000, 2002; Zheng & Miralda-Escudé 2002; Richling 2003; Cantalupo et al. 2005; Dijkstra et al. 2006; Hansen & Oh 2006; Tasitsiomi 2006; Verhamme et al. 2006, 2015; Laursen et al. 2013; Behrens et al. 2014; Duval et al. 2014; Gronke et al. 2015; Smith et al. 2019; Lao & Smith 2020; Michel-Dansac et al. 2020). Meanwhile, a galaxy model needs to be constructed to perform such a radiative transfer calculation. One can adopt a realistic galaxy model from hydrodynamical simulations. Galaxies from such simulations can be useful for performing a statistical study of Lyα properties, but they cannot be directly used to model individual galaxies in observations. Therefore it would be better to adopt a simple but manageable toy model for the purpose of reproducing observations. One example for such models is a shell model, in which a central Lyα source is surrounded by a constantly expanding, homogeneous, spherical shell of H i medium with dust. Although this shell model has surprisingly well reproduced many observed Lyα line profiles (e.g., Ahn 2004; Schaerer & Verhamme 2008; Verhamme et al. 2008; Schaerer et al. 2011; Gronke et al. 2015; Yang et al. 2016; Gronke 2017; Karman et al. 2017), because of its extreme simplicity and contrivance, there is still room for improvement (e.g., see Section 7.2 in Yang et al. 2016; Orlitová et al. 2018).
Citation: Duval et al. 2014
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Citation: Harrington 1973
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Citation: Laursen et al. 2013
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Along with the time-domain detections, we identified J173438.35-504550.4 as a potential host galaxy for FRB 20201123A using robust statistical treatment given the relatively small localization error region. At face value, the low redshift of J173438.35-504550.4 appears at odds with the large dispersion measure for FRB 20201123A (${\rm DM}_{\rm FRB}\approx 434 \, {\rm pc \, cm^{-3}}$). Our Galaxy, however, contributes ${\rm DM}_{\rm ISM}\approx 200 \, {\rm pc \, cm^{-3}}$ (NE2001 gives 229 ${\rm pc \, cm^{-3}}$ and YMW16 gives 162 ${\rm pc \, cm^{-3}}$) from its interstellar medium and a presumed ${\rm DM}_{\rm Halo}\sim 50 \, {\rm pc \, cm^{-3}}$ from its halo (Prochaska & Zheng 2019). This leaves ${\approx}180 \, {\rm pc \, cm^{-3}}$ for the cosmos (DMcosmic) and the host (DMhost). At z = 0.05, the average cosmic contribution is $\langle {\rm DM}_{\rm cosmic}\rangle \sim 42\, {\rm pc \, cm^{-3}}$ (Macquart et al. 2020) but the intrinsic scatter in this quantity is predicted to be large. Adopting the best-fitting model to the Macquart relation by Macquart et al. (2020), the 95 per cent confidence interval is ${\rm DM}_{\rm cosmic}= [15,125] \, {\rm pc \, cm^{-3}}$. Allowing for the maximum value of this interval (which would imply a significant foreground galaxy halo), we recover a minimum host contribution of ${\rm DM}_{\rm host, min} \approx 60~\rm pc~cm^{-3}$. This is consistent with estimates for host galaxy contributions from theoretical and empirical treatments (Prochaska & Zheng 2019; James et al. 2022). For a true DMcosmic value of this sightline closer to (or below) the mean, the host contribution would exceed $100 \, {\rm pc \, cm^{-3}}$. Such values are inferred for other FRB hosts (e.g. FRB 20121102A; Tendulkar et al. 2017). In conclusion, we find no strong evidence to rule out the association with J173438.35-504550.4 based on its redshift and DMFRB. The significant host contribution to the DM, combined with the scattering in FRB 20201123A possibly originating in the host, shows that it shares similarities with other highly active, repeating FRBs like FRB 20121102A and FRB 20190520A and potentially resides in a turbulent and dense environment within the host.
Citation: Prochaska & Zheng 2019
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Citation: Prochaska & Zheng 2019
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It is generally thought that the stellar halo of the Milky Way (MW) contains the fossil record of the MW’s formation history imprinted in its kinematical and chemical properties. Since Eggen et al. (1962) showed a model of the MW’s formation process from the kinematic analysis of halo stars in the solar neighborhood, many studies of this Galactic fossil component have been carried out using a large number of newly available halo samples, with their assembly requiring considerable observational efforts over the past decades. These include, for example, the Hipparcos Catalog, obtained from the first astrometry satellite (Perryman et al. 1997), and spectroscopic catalogs such as RAVE (Steinmetz et al. 2006), SEGUE (Yanny et al. 2009), LAMOST (Cui et al. 2012; Zhao et al. 2012), GALAH (De Silva et al. 2015), APOGEE (Majewski et al. 2017), H3 (Conroy et al. 2019), and more. Perhaps the most significant impacts on this field of research have been brought about by the second astrometry satellite, Gaia. The Gaia catalog (Gaia Collaboration et al. 2016, 2018, 2021) provides trigonometric parallaxes and proper motions for billions of Galactic stars with unprecedented high accuracy. Based on the astrometry data of the stars in the Gaia catalog combined with that of the spectroscopic catalogs, the MW’s new dynamical maps have been drawn (e.g., Belokurov et al. 2018; Helmi et al. 2018; Myeong et al. 2018a, 2018b; Beane et al. 2019; Hagen et al. 2019; Iorio & Belokurov 2019; Anguiano et al. 2020; Cordoni et al. 2021; Koppelman et al. 2021), and new aspects of the MW’s formation and evolution history have been revealed (e.g., Antoja et al. 2018; Sestito et al. 2019; Wyse 2019). In particular, the Gaia catalog has enabled the discovery of new substructures in the MW’s stellar halo (e.g., Koppelman et al. 2019; Myeong et al. 2019; Li et al. 2020) that are remnants of past merging/accretion events associated with the MW’s formation history (e.g., Fernández-Trincado et al. 2020; Naidu et al. 2020, 2021).
Citation: Cui et al. 2012
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In the upper panel of Fig. 17, we demonstrate that the mass distribution of GMCs in each galaxy reproduces the upper limit of ∼3 to 8 × 106 M⊙ observed by Rosolowsky et al. (2003) in M33, by Freeman et al. (2017) in M83 and by Miville-Deschênes et al. (2017) and Colombo et al. (2019) in the Milky Way. This upper limit has been predicted to arise due to a combination of centrifugal forces and stellar feedback (Reina-Campos & Kruijssen 2017). We also find a turnover in the mass spectrum between 104.8 and 105 M⊙, consistent with the behaviour of the GMC mass distribution in the Milky Way (Miville-Deschênes et al. 2017), although we cannot rule out the possibility that the turnover we see in the simulations is influenced by their limited mass resolution. Above the turnover, the GMC mass function has a power-law form with β ∼ 1.9, close to the observed range of β ∈ [1.6, 1.8] for clouds in the Milky Way (Solomon et al. 1987; Williams & McKee 1997; Heyer et al. 2009; Roman-Duval et al. 2010; Miville-Deschênes et al. 2017; Colombo et al. 2019) over the same mass range (log M ∈ [4.8, 6.5]). In the lower panel of Fig. 17, we display the spectrum of GMC sizes for each simulated galactic disc, given by the effective cloud radius ℓeff, such that
(42)$$\begin{eqnarray*}
\ell _{\rm eff} = 1.91 \sqrt{\Delta \ell _{\rm maj}^2 + \Delta \ell _{\rm min}^2},
\end{eqnarray*}$$where Δℓmaj and Δℓmin are the second moments of an ellipse fitted to the footprint of each cloud in the galactic mid-plane, using astrodendro. We adopt this definition of the cloud size in order to make a direct comparison to works in the existing observational literature (e.g. Solomon et al. 1987; Bertoldi & McKee 1992; Rosolowsky & Leroy 2006; Colombo et al. 2019). The factor of 1.91 is the correction first defined by Solomon et al. (1987) for converting the RMS cloud extent to an estimate of the spherical cloud size. The smallest resolved cloud has a diameter of 18 pc, so we do not capture the observed turnover of the distribution at ∼30 pc (Miville-Deschênes et al. 2017). Likewise, our largest clouds slightly exceed the truncation size of 70 pc observed by Colombo et al. (2019), with a maximum diameter of ∼200 pc. Importantly, we do approximately reproduce the observed power-law slope of $\mathrm{d}N/\mathrm{d}R \sim R^{-\beta _\ell }$ with βℓ ∼ 2.8 (Colombo et al. 2019). This is given by the black line in Fig. 17, while our best fit to the simulation data over the observed range of cloud sizes ℓeff ∈ [18, 70] pc is given by the purple line, with a slightly shallower slope of βℓ = 2.43 ± 0.06.
Citation: Roman-Duval et al. 2010
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In the following, we consider an NS with mass MNS = 1.5 M⊙ and radius RNS = 12 km, which is compatible with expectations from modern equations of state such as APR or BSk21 models (Akmal, Pandharipande & Ravenhall 1998; Goriely, Chamel & Pearson 2010). The value of the radius is also in agreement with the estimates derived by Sartore et al. (2012) and Ho et al. (2007), assuming a source distance of 120 pc (Walter et al. 2010). This choice translates into a gravitational redshift factor at the star surface 1 + z = 1.26. The rotational period of RX J1856 is P = 7 s and the X-ray pulsed fraction is the lowest among the XDINSs, ∼1.3 per cent (Tiengo & Mereghetti 2007). The polar strength of the dipole field is taken to be Bp = 1013 G, a value which is somehow intermediate between the spin-down measure and the estimates from spectral fitting (Ho et al. 2007; van Kerkwijk & Kaplan 2008). We assume that the magnetic field is dipolar (see Section 2) and that the surface temperature distribution is that induced by the core-centred dipole. Since for fields higher than ∼1011 G, electron conduction across the field lines is strongly suppressed, the meridional temperature variation is Tdip ≃ Tp|cos θB|1/2, where Tp is the polar value of the temperature (e.g. Greenstein & Hartke 1983). We checked that this simple expression for Tdip differs only slightly ( ≲ 6  per  cent) from the more accurate formula by Potekhin, Pons & Page (2015) for θ ≲ 80°. However, taken face value, the previous expression for Tdip yields vanishingly small values near the magnetic equator. The analysis of Sartore et al. (2012) has shown that the X-ray spectrum of RX J1856 is best modelled in terms of two blackbody components with $kT^\infty _\mathrm{h}\sim 60$ eV and $kT^\infty _\mathrm{c}\sim 40$ eV. To account for this in a simple way, we actually adopt a temperature profile given by Ts = max (Tdip, Tc) with Tp = Th, where $T_\mathrm{h,c}=T^\infty _\mathrm{h,c}/(1+z)$.
Citation: Tiengo & Mereghetti 2007
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We show the mass-loading factors required to simultaneously reproduce the stellar metallicity and SFR of passive galaxies in the bottom panel of Fig. 9. We find that the mass-loading factor strongly decreases with increasing stellar mass. This anticorrelation between stellar mass and λeff is qualitatively consistent with the mass dependence in theoretical models, which predict that $\lambda \propto M_*^{-1/3}$ (Murray, Quataert & Thompson 2005) for momentum-driven winds and $\lambda \propto M_*^{-2/3}$ for energy-driven winds (e.g. Dekel & Silk 1986), as well as other observational evidence (e.g. Heckman et al. 2015; Chisholm et al. 2017; Fluetsch et al. 2019). Our results indicate that ‘effective’ outflows (which are capable of permanently removing gas from the galaxy) are, together with starvation, of increasing importance in low-mass galaxies. In particular, since the rate at which gas is locked up into long-lived stars is given by (1 − R)Ψ = 0.575Ψ, and the rate at which gas is ejected from the galaxy is given by λeffΨ, then, for galaxies with log (M*/M⊙)  10.2, the rate at which gas is lost through galactic winds is roughly 1–3 times larger than the rate at which gas is locked up into long-lived stars. Clearly outflows play an essential role in depleting the gas reservoir of low-mass galaxies during the quenching phase. Outflows are relatively weaker (λeff ≤ 0.6) in more massive galaxies (log (M*/M⊙) > 10.2), with starvation becoming the dominant quenching mechanism, as illustrated in Fig. 9. However, outflows still play an important role in quenching star formation, as comparable amounts of gas are removed through galactic winds and through the formation of long-lived stars. Although these massive galaxies may be ejecting large amounts of gas in the form of outflows (i.e. a large λ), our results suggest that these ejection events are short lived and/or most of the outflowing gas does not escape the galaxy and is instead recycled (i.e. a relatively small λeff).
Citation: Chisholm et al. 2017
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As we describe in Section 2 the case of TNDWs propagating in a plasma with equal mass fraction of 12C and 16O (CO) and density of ρ0,7 ≈ 1,1 which is typical for Type Ia supernovae, is particularly challenging for full-star simulations. In addition to the problem that the burning length-scale is much smaller than the typical cell size, near detailed balance is obtained for many isotopes while NSE is not reached. We test in Section 2 two available one-dimensional (1D) codes: a modified version of the 1D, Lagrangian version of the vulcan code (hereafter V1D; for details, see Livne 1993) and a modified version of the Eulerian, 1D hydrodynamic flash4.0 code with thermonuclear burning (Fryxell et al. 2000; Dubey et al. 2009), against the ρ0,7 = 1 case. We show that with resolutions that are typical for multidimensional full-star simulations, the V1D and the flash results are not satisfactory (up to $50{{\ \rm per\ cent}}$ error in V1D and up to $20{{\ \rm per\ cent}}$ error in flash). We demonstrate in Section 3 the operation of a new numerical scheme for thermonuclear burning that can be implemented in multidimensional full-star simulations. The new scheme allows an accurate calculation of TNDWs in a consistent way (i.e. without pre-describing the position and/or the conditions behind the TNDW) with all thermonuclear burning taking place in situ (without post-processing) for an arbitrary reaction network with hundreds of isotopes. The new scheme contains two important ingredients: (1) a burning limiter (a variant of Kushnir et al. 2013), which guarantees that the thermodynamic variables and the composition are accurate for the resolved scales, while keeping the numerical thermodynamic trajectory for unresolved scales within some controlled error from the true thermodynamic trajectory, and (2) adaptive statistical equilibrium (ASE) burning, which groups isotopes that are in detailed balance into one effective isotope, where the ratio between the isotope abundances inside the group is given from equilibrium conditions (this is an extension of the earlier attempts of Hix et al. 2007; Parete-Koon & Hix 2008; Parete-Koon, Hix & Thielemann 2008, 2010).
Citation: Dubey et al. 2009
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Over the last 15 yr, our understanding of how galaxies form and evolve has improved substantially. The combination of technological and theoretical progress has brought this field into a new era: advances in observational techniques (e.g. multi-object spectroscopy, efficient near-infrared CCDs) have enabled multiwavelength observations of large samples of galaxies out to the highest redshifts, while the steady rise of computational power and refinement of numerical techniques have fostered new approaches (e.g. semi-analytic models, hydro-dynamic simulations) to model the formation and evolution of galaxies. This progress has led to a general consensus about the main physical ingredients required to describe the evolution of the galaxy population (e.g. Gonzalez-Perez et al. 2014; Lu et al. 2014; Vogelsberger et al. 2014; Henriques et al. 2015; Schaye et al. 2015): collapse and hierarchical growth of dark matter haloes; accretion of baryons on to these haloes; conversion of baryons into stars; feedback of massive stars and active galactic nuclei (AGN) on star formation; supernova- and AGN-driven outflows of metal-enriched gas; infall of both pristine and metal-enriched gas on to galaxies. The large-scale environment can also affect galaxy properties, in particular, by providing quenching mechanisms (e.g. tidal or ram-pressure stripping, strangulation; e.g. Lagos et al. 2014; Rafieferantsoa et al. 2015), and through its influence on the merger rate (e.g. Lackner et al. 2012; Rafieferantsoa et al. 2015) and galactic spins (e.g. Hahn, Teyssier & Carollo 2010; Codis et al. 2012). Although these different ingredients are present in many galaxy formation models, we still lack a detailed quantification of their respective roles in shaping the properties of galaxies. This is because of the complexity inherent in galaxy physics, which combines gravity, radiation hydro-dynamics, magnetic fields and high-energy physics, acting on scales from less than a pc (e.g. for the formation of proto-stellar cores) to over a Mpc (e.g. for environmental effects). For this reason, ‘first-principles’ simulations of galaxy formation remain far beyond the reach of current computational capabilities. Instead, small-scale baryonic physics is generally subsumed into sub-grid prescriptions, which vary from model to model (e.g. Scannapieco et al. 2012; Haas et al. 2013a,b; Vogelsberger et al. 2013; Torrey et al. 2014; Crain et al. 2015). The appropriateness of such prescriptions, and hence, our ability to understand galaxy formation, must be assessed by comparing simulated and observed galaxy properties.
Citation: Lu et al. 2014
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