adsabs/FOCAL huggingface dataset:
The large values of α (significantly larger than unity) observed in clouds of low column densities or masses are often interpreted in terms of the presence of a large external confining pressure (P/





 





 e.g., Keto & Myers 1986; Oka et al. 2001; Field et al. 2011; Leroy et al. 2015; Traficante et al. 2018a), although it is hard to imagine that such high pressures can be thermal in general, since the mean ambient thermal pressure in the interstellar medium (ISM) is rather low, 





 





, and large deviations from it occur very infrequently (e.g., Boulares & Cox 1990; Jenkins 2004; Jenkins & Tripp 2011). Instead, it is most likely that these values correspond to ram pressure, in which case they imply mass, momentum, and energy flux across Eulerian cloud boundaries, or a displacement of Lagrangian boundaries (Ballesteros-Paredes et al. 1999; Banerjee et al. 2009). Indeed, in a previous study (Camacho et al. 2016), we find, through measurement of the mean velocity divergence within the clouds in numerical simulations of cloud formation and evolution, that roughly half the clouds with an excess of kinetic energy are undergoing compression. This can be interpreted as the clouds being subject to a ram pressure (which amounts to an inertial compression) that is making them denser and smaller, so that they eventually will become gravitationally bound. The origin of this ram pressure can be large-scale turbulence, a large-scale potential well, or other instabilities. Furthermore, one important possibility is that clouds may be falling into the potential well of a stellar spiral arm, which is the main source of large-scale compression for the gas in the Galactic disk (Roberts 1969). Thus, this is not really a “confinement,” since the clouds are not at rest. The same goes for the other half of the clouds, which are undergoing expansion. In this case, the excess of kinetic energy corresponds to the expansion motions, and again the cloud is not confined, so there is no need for a high confining pressure. In Camacho et al. (2016) and Ballesteros-Paredes et al. (2016, hereafter BP+18) it has been suggested that, for clouds formed by inertial compressions in the background medium (Ballesteros-Paredes et al. 1999), and which gradually become more strongly gravitationally bound, while the inertial compressive motions decay or dissipate, the kinetic energy transits from being dominated by the inertial motions to being dominated by the gravitationally driven motions (see also Collins et al. 2012). In that case, an initial decay of the Larson ratio and the virial parameter may be expected.
Citation: Banerjee et al. 2009
Citation function: Uses
The identification and confirmation of extragalactic SNRs are mainly done using data from the radio (Lacey, Duric & Goss 1997; Hyman et al. 2001; Lacey & Duric 2001), visible (Matonick & Fesen 1997; Matonick et al. 1997; Gordon et al. 1998; Blair & Long 2004; Sonbas, Akyuz & Balman 2009; Sonbaş et al. 2010), and X-Ray (Pence et al. 2001; Ghavamian et al. 2005) wavelength ranges. Only a few extragalactic SNRs have been identified in the infrared using the [Fe ii] 1.64 $\hbox{$\mu $m}$ emission line (Greenhouse et al. 1997). The radiation of SNRs in different wavelength ranges is under the influence of biases as they cover different aspects of the ISM environment and SNR age and evolution (Pannuti et al. 2000; Leonidaki, Zezas & Boumis 2010; Sánchez-Ayaso et al. 2012; Long 2017). In the radio, only SNR candidates associated with H ii regions are identified, which means that radio SNR samples are biased towards star-forming regions. In the X-Ray, candidates are selected if they display a soft spectrum and if they are associated with an H ii region, which means that X-Ray samples have the same bias than the radio samples and are also biased against SNR candidates with hard spectra and no optical counterparts. In the optical, samples are under the influence of biases privileging the identification of SNRs located in low-density environments. A multiwavelength (X-Ray, optical, and radio) study of SNRs in NGC 300 by Pannuti et al. (2000) revealed 16 new SNRs, 2 in the radio, and 14 in the X-Ray, in addition to the 28 SNRs previously identified in the optical. The lack of new optical detection is explained by the fact that optical SNRs can only be detected when they represent relatively low confusion with other H α emission sources. The optical SNRs found here are generally located well away from star-forming regions. Consequently, SNR samples identified optically are often not complete. Another technique, based on the search for Large-Velocity-Width Sources (LVWS), is used to identify SNRs with optical spectroscopy (Chu & Kennicutt 1986; Castaneda, Vilchez & Copetti 1990). While H ii regions show low-velocity dispersion (σv ≤ 30 km s−1; Melnick 1977; Gallagher & Hunter 1983), broad emission line widths observed in the LVWS can be caused by stellar winds or SN. Using this technique, Chu & Kennicutt (1986) discovered four LVWS inside the Giant H ii regions NGC 5471 A, B, C, and NGC 5461 in the nearby galaxy M101.
Citation: Long 2017
Citation function: Background
According to chemical models, the molecules HCS+ and CS are among the most closely related species present in the ISM, as they participate in a direct exchange in both the formation and destruction of CS (Drdla et al. 1989; Lucas & Liszt 2002). In the reaction network considered by Drdla et al. (1989) and Lucas & Liszt (2002), CS is believed to be formed primarily from the dissociative recombination reaction of (6)\begin{equation} \label{eq:HCSdisRecomb} \text{HCS}^+ + {\rm e}^- \rightarrow \text{CS} + \text{H}, \end{equation}HCS++e−→CS+H,where HCS+ is first formed by reactions beginning with S+. However, recent experiments indicate that this product channel occurs in only 19 percent of collisions, with 81 percent of collisions forming CH + S (Montaigne et al. 2005). Three mechanisms dominate the destruction of CS in these networks. In the first two, photoionization and ion-exchange reactions destroy CS to form CS+, by (7)\begin{equation} \label{eq:CSphotoIo} \text{CS} + \gamma \rightarrow \text{CS}^+ + {\rm e}^- , \end{equation}CS+γ→CS++e−,and (8)\begin{equation} \label{eq:CSionExchange} \text{CS} + X^+ \rightarrow \text{CS}^+ + X, \end{equation}CS+X+→CS++X,where X+ is a cationic species and X is the corresponding neutral species. CS+ then quickly reacts to form HCS+: (9)\begin{equation} \label{eq:CSio_recomb} \text{CS}^+ + \text{H}_2 \rightarrow \text{HCS}^+ + \text{H} . \end{equation}CS++H2→HCS++H.In the third destruction route, CS reacts with H\hbox{$_3^+$}+3 to form HCS+ directly: (10)\begin{equation} \label{eq:CSandH3plus} \text{CS} + \text{H}_3^+ \rightarrow \text{HCS}^+ + \text{H}_2 . \end{equation}CS+H3+→HCS++H2.As CS is formed by a reaction of HCS+ and the dominant destruction pathways for CS form HCS+, we would expect the abundances of CS and HCS+ to be delicately balanced, and for the two abundances to track each other. While chemical models of sulfur-bearing chemistry in diffuse clouds underpredict the abundance of HCS+ by multiple orders of magnitude, Lucas & Liszt (2002) determined that if the observed abundance of HCS+ is injected into a diffuse cloud, it is possible to account for the CS abundances observed in their sample, further emphasizing the theoretical importance of NCS/NHCS+. In clouds in the Galactic center, where H\hbox{$_3^+$}+3 is ≳10 times more abundant compared to diffuse clouds in the disk (Oka et al. 2005), we might expect an offset in the ratio, tending toward a higher relative abundance of HCS+.
Citation: Montaigne et al. 2005
Citation function: Compare/Contrast
Simultaneously solving the MHD equations and the global angle- and energy-dependent radiation transport equation, in general relativity, is both computationally expensive (typically prohibitively so) and technically challenging. Even so, significant progress has been made in the last decade, though the problem is usually made tractable by introducing at least one of the following simplifying assumptions: abandoning general relativity in favor of a pseudo-Newtonian description of the gravitational potential while performing realistic, multi-angle group radiation transport (Jiang et al. 2014a, 2014b, 2019a, 2019b); limiting the possible angular dependence of the radiation field by invoking either flux-limited diffusion (Zanotti et al. 2011; Roedig et al. 2012) or, more recently, the “M1 closure” relation in either axisymmetric (2D; Sadowski et al. 2014) or 3D simulations (Fragile et al. 2012, 2014; McKinney et al. 2014; Sadowski et al. 2016); or using Monte Carlo (Ryan et al. 2015) / hybrid Monte Carlo techniques (Ryan & Dolence 2020). Most attempts have eschewed energy-dependent transfer in favor of a “gray” atmosphere—the radiation field is treated as monochromatic, coupled to the fluid only through the Rosseland mean opacity (Rybicki & Lightman 1986). The first of these approximations, the pseudo-Newtonian potential, is especially problematic in regions close to the black hole, where general relativistic effects play a critical role in determining both the structure of the accretion flow and the photon trajectories. The others are essentially variants of a diffusion approximation and are best suited to the cooler, denser, and optically thick body of the accretion disk, where the environment is similar to those found in stellar atmospheres—the field from which these methods, and gray transfer, originate (Chandrasekhar 1960). With the exception of Monte Carlo methods (Ryan et al. 2015; Ryan & Dolence 2020), these are all especially poorly suited to the diffuse, hot, optically thin corona, especially at small radii near the black hole.
Citation: Jiang et al.; 2014b
Citation function: Background
At each heliocentric distance rh, the activity model (Fulle et al. 2020b) is defined by five analytical equations fixing (i) the gas pressure P(s) depending on the depth s from the nucleus surface (Fig. 1 for the CO2 case), (ii) the gas flux Q from the nucleus surface, (iii) the temperature gradient ∇T at depths of a few cm, (iv) the heat conductivity λs at depths of a few cm below the nucleus surface, and (v) the temperature Ts of the nucleus surface
(3)$$\begin{eqnarray*}
P(s) = P_0 ~f(s) ~\exp \left[{- {T_0 \over {T_s - s ~\nabla T}}}\right]
\end{eqnarray*}$$(4)$$\begin{eqnarray*}
Q = {{14 ~r ~P(R)} \over {3 ~R}} \sqrt{{2 ~m} \over {\pi k_B ~(T_s - R ~\nabla T)}}
\end{eqnarray*}$$(5)$$\begin{eqnarray*}
\nabla T = {\sqrt{\Lambda ~Q ~/ ~\sigma _B} \over {8 ~(T_s - R ~\nabla T) ~R}}
\end{eqnarray*}$$(6)$$\begin{eqnarray*}
\lambda _s = {32 \over 3} ~(T_s - R ~\nabla T)^3 ~\sigma _B ~R
\end{eqnarray*}$$(7)$$\begin{eqnarray*}
(1 - A) ~I_\odot ~\cos \theta ~r_h^{-2} = \epsilon \sigma _B T_s^4 + \lambda _s ~\nabla T + \Lambda ~Q
,
\end{eqnarray*}$$where P0, T0, and Λ values are listed in Table 1, s is the depth from the nucleus surface, $f(s) = 1 - (1 - {s \over R})^4$ for s ≤ R, f(s) = 1 elsewhere, r ≈ 50 nm and R ≈ 5 mm are the radii of the grains of which cometary dust consists (Levasseur-Regourd et al. 2018; Güttler et al. 2019; Mannel et al. 2019) and of the pebbles of which cometary nuclei consist (Blum et al. 2017; Fulle et al. 2020b), m is the mass of the gas molecule, kB is the Boltzmann constant, σB is the Stefan–Boltzmann constant, A is the nucleus Bond albedo (e.g. A = 1.2 per cent measured at 67P; Fornasier et al. 2015), I⊙ is the solar flux at the heliocentric distance of Earth, θ is the solar zenithal angle, and ϵ ≈ 0.9 is the nucleus emissivity. Since the gas originates from the superficial pebbles and is assumed to share the temperature Ts − s ∇T of refractories and ices, the thermal diffusion due to gas convection is negligible with respect to the sublimation sink Λ Q. A nucleus is active if the gas pressure overcomes the tensile strength S bonding dust particles to the nucleus surface (Skorov & Blum 2012), thus defining the activity onset for each ice (Table 2), occurring (i) at rh = 85 au for carbon monoxide (Fulle et al. 2020a); (ii) at rh = 60 au for molecular oxygen; (iii) at rh = 52 au for methane; (iv) at rh = 18 au for ethane; (v) at rh = 13 au for carbon dioxide (dotted line in Fig. 1); and (vi) at rh = 3.8 au for water (Fulle et al. 2020b; Ciarniello et al. 2021). The value R ≈ 5 mm has been constrained by several data collected at comet 67P, by laboratory experiments of dust accretion in conditions expected to occur in the solar protoplanetary disc and by observations of other protoplanetary discs (Blum et al. 2017). Other R-values would not provide the best fit of the 67P water-loss time-evolution (Ciarniello et al. 2021).
Citation: Blum et al. 2017
Citation function: Uses; Uses
Citation: Blum et al. 2017
Citation function: Uses
As we said in the previous section, the evolution of the magnetic fields in the two domains (the disk and the magnetosphere) are related. It is important to note here that there is an important complication that enters into the matching between the two domains and this has to do with the abrupt transition between the dense turbulent accreting flow and the almost empty ionized (probably also evaporating and outflowing) magnetosphere. This is very similar to the well known transition region in the Sun where, within a very thin layer of about 100 km, the temperature rises by almost two orders of magnitude to about one million degrees, and the matter density drops by about one order of magnitude. The solar transition region is the subject of ongoing intense theoretical and numerical investigations. The astrophysical disk transition region is at least equally complicated since it is not only the base of the disk corona, but also the origin of purported disk winds and outflows (e.g., Ferreira & Pelletier 1995; Li 1995). It is not the aim of the present study to solve in detail for the structure of the flow and magnetic field in this region. In practice, we define a transition region of two θ-grid zones above 





. In that region we set
14





This matching allows for magnetic field loops that reach the disk surface zones to escape to the magnetosphere. We also set the field lines in that region in rotation by introducing a magnetospheric poloidal electric field
15





Finally, we slightly modify the induction equation that we solve for the disk material in that region by introducing an extra phenomenological term that mimics various complex physical effects taking place in the surface layers of astrophysical accretion disks such as surface convection, buoyancy of magnetic field loops, and the fact that the surface layers are highly ionized (thus also fully conducting) due to cosmic ray irradiation, namely
16





where, 





 is the disk magnetic field in the transition layer. The introduction of this transition layer effectively shields the disk interior: (a) it prevents any generated magnetospheric field from “entering” the disk from above (especially in the case of turbulent high η flow conditions), and (b) it “pushes outward” (in the θ direction) at a very small fraction of the speed of light any magnetic field loop that enters it from below. We implemented different fractions (





, 





, etc.) and the results were qualitatively similar. As we said above, we are not claiming that we study in detail the disk-magnetosphere transition region. This is the reason we opted for a minimal two θ-zone layer that only serves the purpose of shielding the disk interior from the disk magnetosphere. Note that Parfrey et al. (2015) ignored the important physical significance of a transition layer by directly coupling their magnetosphere to a prescribed distribution of the magnetic field on the surface of the accretion disk without solving for the magnetic field distribution in the disk interior.
Citation: Li 1995
Citation function: Motivation
We note that it is unlikely that poloidal magnetic fields are responsible for the observed RMs of M87 because in that case one expects ρ ∝ r0 from B ∝ r−2, which is impossible to explain with the accretion models currently available (Yuan & Narayan 2014). However, there is indication of non-negligible poloidal fields as well as toroidal fields—resulting in helical magnetic fields—in the jet environment of other distant AGNs, which results in transverse RM gradients with no sign changes (e.g., Asada et al. 2002; Zamaninasab et al. 2013; Gómez et al. 2016; Gabuzda et al. 2018, see also Section 3.5.2). The existence of non-negligible poloidal fields was indicated even for the M87 jet at HST-1 from the observed moving knots with both fast and slow velocities, which could be explained by quad relativistic MHD shocks in a helical magnetic field permeating the jet (Nakamura et al. 2010; Nakamura & Meier 2014). In Sections 3.5.2 and 4.1, we explained that poloidal magnetic fields might be very weak at distances ≳5000 rs probed in this study and we concluded that hot accretion flows and winds are more probable to be the Faraday screen than the jet sheath. However, if the jet experiences recollimation, which may lead to formation of standing shocks (e.g., Daly & Marscher 1988; Gómez et al. 1995; Agudo et al. 2001; Mizuno et al. 2015; Martí et al. 2016; Fuentes et al. 2018), then the strength of poloidal fields could be substantially enhanced. Indeed, the width of HST-1 is significantly smaller than expected from the parabolic (conical) width profile inside (outside) the Bondi radius (Asada & Nakamura 2012), which has been explained with a hydrodynamic recollimation shock (e.g., Stawarz et al. 2006; Bromberg & Levinson 2009; Asada & Nakamura 2012). Also, the core of blazars is often identified with a recollimation shock (e.g., Daly & Marscher 1988; Marscher 2008; Cawthorne et al. 2013). This may explain the presence of non-negligible poloidal fields in the sheath of blazar jets and in HST-1, but not in the M87 jet inside the Bondi radius.
Citation: Gabuzda et al. 2018
Citation function: Background
However, a significant outstanding problem of using red giants is that modeling their individual frequencies is too time consuming for the analysis of tens of thousands of stars. We therefore rely on using asteroseismic scaling relations, 






ν


max


∝


gT


eff


−
1

/

2




 and 




Δ
ν
∝
ρ


 (Brown et al. 1991; Kjeldsen & Bedding 1995), to estimate their radius and mass (and hence age). Here, 






ν


max




 is the frequency of maximum amplitude and 




Δ
ν


 the average large frequency separation. These relations assume that the structure of a red giant star is homologous with respect to the Sun. In reality this assumption is not strictly correct and verification of the relations is ongoing. However, the independent high-precision estimates of mass and radius required for this verification are difficult to obtain. For subgiants and dwarfs, the 






ν


max




 scaling relation has been shown to work well (Bedding 2014) and recently, Coelho et al. (2015) found the proportionality 






ν


max


∝


gT


eff


−
1

/

2




 to be accurate to within 1.5%. Using Hipparcos parallaxes and/or interferometry, the asteroseismic radii calculated from scaling relations have been found to be accurate to within 5% (Bruntt et al. 2010; Huber et al. 2012; Silva Aguirre et al. 2012). For giants we generally do not have accurate parallaxes, so such studies are awaiting results from Gaia (Perryman 2002). Open clusters have been used to test the scaling relations for giants (Brogaard et al. 2012; Miglio et al. 2012; Sandquist et al. 2013). Miglio et al. (2012) found agreement to within 5% for scaling relation-based radii. Testing of masses is more challenging. For a few cases where such verification have been performed, the scaling relation-based masses seem to be overestimated for giants (Miglio et al. 2012; Frandsen et al. 2013; Epstein et al. 2014). For two lower red giant branch stars (Epstein et al. 2014) find evidence that the mass estimated by using only 




Δ
ν


 (but with additional 






ν


max




 independent quantities) is lower compared to using both 




Δ
ν


 and 






ν


max




. Based on this they suggested that a modification to the 






ν


max




 scaling relation might be required. Theoretical modeling has suggested corrections to the 




Δ
ν


 scaling relation (Stello et al. 2009; White et al. 2011; Miglio et al. 2013a), but there has been no comprehensive study to verify the corrections. In relation to 






ν


max




, Houdek et al. (1999) and Chaplin et al. (2008) had suggested theoretically that 






ν


max




 coincides with the plateau of the damping rate with frequency. Belkacem et al. (2011) confirmed this for the Sun using SoHO GOLF observations. Balmforth (1992) suggested that this is caused by a resonance between the thermal adjustment time of the superadiabatic boundary layer and the mode frequency, which was also confirmed by the theoretical study of Belkacem et al. (2011; see also Belkacem 2012; Belkacem et al. 2013). However, there is currently no way to accurately predict 






ν


max




 from theory.
Citation: Brogaard et al. 2012
Citation function: Background
This paper presents a new 3D RT code, RAdiation SCattering in Astrophysical Simulations (RASCAS), which was designed to construct accurate multi-wavelength mock observations (spectra, images, or datacubes) from high-resolution simulations. RASCAS deploys a general two-step methodology (e.g. Hummels et al. 2017; Barrow et al. 2017). The first step consists in extracting all relevant information from the simulation outputs: (1) the information concerning the medium through which light will propagate, and (2) the information concerning the sources of radiation. Point (1), for example, determines the number density of H I atoms, their thermal velocity dispersion and bulk velocity, and the dust density, everywhere in a chosen volume. Fully coupled radiation-hydrodynamic simulations would naturally provide the information about ionised states, but retrieving this information in pure hydrodynamic simulations may be tricky. In such case, it is necessary to process the simulation outputs with additional software and models, typically with CLOUDY (Ferland et al. 2013), as in Hummels et al. (2017) and Barrow et al. (2017), or with independent codes which solve for H and He ionisation by propagating ionising radiation in post-processing (e.g. Li et al. 2008; Yajima et al. 2012). Point (2) is also involved to a greater or lesser extent, depending on the sources. Computing the continuum emission from star particles is relatively straightforward, using spectro-photometric models of stellar populations (e.g. Bruzual & Charlot 2003; Eldridge et al. 2017). However, computing the emission lines from the gas (e.g. in the Lyman-α line or in other nebular lines) again requires a detailed knowledge of the ionisation and thermal state of the emitting species. This may be provided by the simulation code, as is the case for H and He with RAMSES-RT (Rosdahl et al. 2013), or it may be necessary to post-process the simulation to estimate the emissivities of the gas. This first step is very simulation- and model-dependent, and RASCAS chooses to encapsulate it in a simulation-plugin module and to implement two stand-alone pre-processing codes which generate an adaptive mesh with all the needed physical information about the gaseous medium, and the initial conditions for light emission in the form of lists of photon packets. These datasets, which could easily be generated from other simulations with any post-processing code, serve as inputs to RASCAS to perform the radiative transfer computation.
Citation: Hummels et al. 2017
Citation function: Uses