adsabs/FOCAL huggingface dataset:
Outflows present similarly confounding puzzles, e.g. the existence and survival of cool and low-ionization gas moving at high speeds in observed outflows (e.g. Veilleux, Cecil & Bland-Hawthorn 2005; Tremonti, Moustakas & Diamond-Stanic 2007; Zhang et al. 2017; Cashman et al. 2021) and high-velocity clouds (HVCs; e.g. Putman et al. 2012; Richter et al. 2017). These clouds should be shredded and destroyed before they can attain the observed velocities if they are accelerated by the ram pressure of a hot wind (e.g. Zhang et al. 2017). Possibly the material is accelerated by radiation pressure (e.g. Murray, Quataert & Thompson 2005; Murray, Ménard & Thompson 2011; Hopkins, Quataert & Murray 2012) or cosmic rays (CRs; e.g. Everett, Zweibel & Benjamin 2008; Brüggen & Scannapieco 2020; Quataert, Jiang & Thompson 2022), but alternatively the high-speed cool gas may be due rapid radiative cooling in some situations. If a cloud is sufficiently large, then mixing of the cold cloud with the hotter wind can lead to a region of mixed material with a cooling time less than the cloud-destruction time, and this cooling region can cause a cloud’s mass to grow (Armillotta, Fraternali & Marinacci 2016; Gronke & Oh 2018, 2020; Schneider, Robertson & Thompson 2018; Kanjilal, Dutta & Sharma 2021, but see caveats in S5.4 of Schneider et al. 2020). This multiphase cloud growth could explain the presence of cool gas at high speeds and the pervasive low- and high-ionization metals found in the CGM (e.g. Tumlinson et al. 2011; Werk et al. 2013; Burchett et al. 2019). Similar conclusions have been reached about inflowing gas: galactic accretion is now recognized to sometimes occur in cold, filamentary streams (Kereš et al. 2005; Dekel et al. 2009), and Mandelker et al. (2020) have shown that if a cold stream has a large enough radius, it can grow in mass due to cooling in a mixing layer. Models of HVCs moving through a galactic halo also find that the HVCs may be disrupted (or grow very little) if they are small (Heitsch & Putman 2009; Marinacci et al. 2010), but they can grow substantially if they are large enough (Fraternali et al. 2015; Gritton, Shelton & Galyardt 2017). Brüggen & Scannapieco (2016) have shown that thermal conduction, while causing evaporation, can actually extend the lifetime of cold clouds (see also Vieser & Hensler 2007 in a somewhat different context).
Citation: Quataert, Jiang & Thompson 2022
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GX 339-4 is a recurrent X-ray transient and the system is a confirmed black hole X-ray binary with a low-mass companion star. Although the black hole mass, the system inclination angle and distance are still unknown, they range between 5.8 and 10 M⊙ (Hynes et al. 2003; Muñoz-Darias, Casares & Martínez-Pais 2008; Shidatsu et al. 2011), 20° and 50° (Miller et al. 2006; Done & Diaz Trigo 2010; Shidatsu et al. 2011) and 6 and 15 kpc (Hynes et al. 2004; Zdziarski et al. 2004; Shidatsu et al. 2011), respectively. The source exhibits multiwavelength variability on a broad range of time-scales (Motch, Ilovaisky & Chevalier 1982; Fender, Hanson & Pooley 1999; Corbel et al. 2003, 2013; Dunn et al. 2008; Gandhi 2009; Casella et al. 2010). In addition, it also shows evidence of relativistic jets (Fender et al. 1997; Corbel et al. 2000; Markoff et al. 2003; Gandhi et al. 2008). The observations we used in this work are part of a multiwavelength study of GX 339-4 (Cadolle Bel et al. 2011; Corbel et al. 2013), and in particular of the first mid-infrared study of the source published in Gandhi et al. (2011) and obtained on 2010 March 11. GX 339-4 was observed with theWide-field Infrared Survey Explorer (WISE; Wright et al. 2010) satellite in four bands (1.36 × 1013, 2.50 × 1013, 6.52 × 1013 and 8.82 × 1013 Hz, respectively, W4, W3, W2 and W1), at 13 epochs, sampled at multiples of the satellite orbital period of 95 min and with a shortest sampling interval of 11 s, when WISE caught the source on two consecutive scans. Radio data were obtained with the Australian Telescope Compact Array during two days – close to but not simultaneous with WISE data – on 2010 March 7 and 14. The mean fluxes are 9.1 ± 0.1 and 9.7 ± 0.1 mJy at 5.5 and 9 GHz, respectively. X-ray data were quasi-simultaneous with WISE, taken between epochs 12 and 13 with the Rossi X-ray Timing Explore (RXTE) satellite. Gandhi et al. (2011) confirm the detection in the mid-infrared of a synchrotron break associated with the compact jet in GX 339-4 (Corbel & Fender 2002), and report the first clear detection of its strong variability. This detection of the jet's intrinsic variability and the overall properties of GX 339-4 make it the ideal source to test our model.
Citation: Gandhi et al. 2008
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We fit the radial dispersion profiles of each UCD to dynamical models using the Jeans Anisotropic Models (JAM) method with the corresponding code discussed in detail in Cappellari (2008). To briefly summarize, the dynamical models are made in a series of steps making two general assumptions: (1) the velocity ellipsoid is aligned with the cylindrical coordinate system (




R
,
z
,
ϕ


), (2) the anisotropy is constant. Here, the anisotropy is defined as 






β


z


=
1
−


(


σ


z



/



σ


R


)


2




 where 






σ


z




 is the velocity dispersion parallel to the rotation axis and 






σ


r




 is the velocity dispersion in the radial direction in the plane of the galaxy. The first step in the dynamical modeling process is to construct a three-dimensional mass model by deprojecting the two-dimensional mass model MGEs discussed in the previous section. In the self-consistent case, the luminosity and mass profile are the same. However, in our case, we used the mass profile to construct the potential and we used the light profile to calculate the observable properties of the model, both described below. The choice to parameterize the light profile with MGEs is motivated by the ease of deprojecting Gaussians and the accuracy in reproducing the surface brightness profiles (Emsellem et al. 1994; Cappellari 2002). The second step in the dynamical modeling process is to construct a gravitational potential using our mass model. This potential also contains a Gaussian to represent a supermassive BH with the axis ratio, q = 1, and width, 




σ
≲


r


min



/

3


, where rmin is the smallest distance from the BH that needs to be accurately modeled. Although a supermassive BH can be modeled by adding a Keplerian potential, it is much simpler to model the BH as this small Gaussian (Emsellem et al. 1994). Next, the MGE formalism is applied to the solution of the axisymmetric anisotropic Jeans equations (see Section 3.1.1 of Cappellari 2008). Finally, the intrinsic quantities are integrated along the LOS and convolved with the PSF from the kinematic data to generate observables that can be compared with the radially binned dispersion profiles. Supermassive BH masses are frequently measured with dynamical models that allow for fully general distribution functions (e.g., Schwarzschild), which is important to include because of the BH mass-anisotropy degeneracy in explaining central dispersion peaks in galaxies. Since plunging radial orbits have an average radius that is far from the center of the galaxy, these orbits can raise the central dispersion without significantly enhancing the central mass density. Similarly, a supermassive BH also raises the dispersion near the center of the galaxy. Other dynamical modeling techniques break this degeneracy by fitting the full orbital distribution without assumptions about the anisotropy. However, given the quality of our kinematic data, a more sophisticated dynamical modeling technique is not feasible; we further discuss the assumptions and limitations of our modeling at the beginning of Section 5.1.
Citation: Emsellem et al. 1994
Citation function: Uses
The ALMA results mentioned earlier (Alonso-Herrero et al. 2020; García-Burillo et al. 2021) add yet another scale given that the KDC contains both an r ∼ 150–200 pc molecular gas ring and an inner r  50 pc torus-like structure. This smaller nuclear disk/ring may correspond to the structure predicted by a radiation-driven fountain model, where AGN radiation feedback induces vertical gas flows that result in a geometrically thick torus (Wada 2012). This mechanism would lead to tori that are a few tens of parsec wide and are dynamic, evolving structures as proposed to interpret several recent observational studies and compilations (e.g., Ramos Almeida & Ricci 2017; Combes et al. 2019; Hönig 2019). Furthermore, Alonso-Herrero et al. (2021) analyzed high-resolution mid-IR imaging of NGC 7582 from VLT/VISIR and found both an unresolved component and an extended polar dust component. These authors argue that these observations can be interpreted with a torus+wind model, according to which IR radiation pressure creates a dusty wind component that contributes to AGN obscuration (e.g., Venanzi et al. 2020). Recent models with a realistic 3D distribution of clumpy dusty material can also reproduce polar mid-IR emission starting with a standard clumpy model, depending on the torus opening angle and scale height (Nikutta et al. 2021). Subparsec-resolution observations will be needed to better constrain torus parameters. For example, NGC 1068 observations with the GRAVITY instrument on the European Southern Observatory Very Large Telescope Interferometer revealed a thin ring with an inner radius of r = 0.24 pc, close to the sublimation radius and inconsistent with a geometrically and optically thick torus on these small scales (Gravity Collaboration et al. 2020). Future high-resolution observations of the nuclear regions of AGN hosts will augment our understanding but will likely still need to be combined with probes on a range of physical scales to establish the full picture of AGN obscuration and AGN outflows.
Citation: Combes et al. 2019
Citation function: Compare/Contrast
Consequently, the neutral gas measured in situ in the coma of comet 67P by the ROSINA experiment (and also MIRO, VIRITS, and Alice) on board Rosetta likely originated several tens of meters beneath the primordial surface of the comet. ROSINA observations provided evidence that this comet is formed from pristine material that has not been significantly altered after its formation in the first Myr of the solar nebula stage. The high abundance of super-volatiles like CO and CO2 (Le Roy et al. 2015), the detection of argon (Balsiger et al. 2015), of molecular nitrogen (Rubin et al. 2015), of molecular oxygen (Bieler et al. 2015), of a high D/H in HDO/H2O and D2O/HDO and HDS/H2S (Altwegg et al. 2015, 2017), and of hydrogen halides (De Keyser et al. 2017; Dhooghe et al. 2017), coupled with the low density, high porosity, and homogeneity of the nucleus (Pätzold et al. 2016) and the absence of signatures of aqueous alteration (see Capaccioni et al. 2015; Davidsson et al. 2016; Quirico et al. 2016; Bardyn et al. 2017) all indicate that comet 67P formed at low temperature and did not experience any substantial global-scale heating after its formation. This suggests that 67P is representative of the solar nebula material from which the solar system had formed. This has strong implications not only for how the measurements made in cometary environments can be used to constrain the protosolar environment but also for the contribution of comets to Earth’s composition. For instance, the measurement of the D/H isotopic ratio in 67P (Altwegg et al. 2015) suggests that comets cannot be considered as the main source of water on Earth. The discovery of significant amounts of O2 in comets (Bieler et al. 2015) was not predicted by astrochemical models and challenges our understanding of the chemistry of molecular clouds and of the protosolar nebula. However, the Jupiter-family comets (JFCs; which include 67P) are a diverse groups. Indeed, even if Giotto measurements indicate that comet 1P/Halley contains similar amounts of O2 (Rubin et al. 2015), different D/H ratios (lower than observed for 67P and compatible with the D/H ratio in the Earth’s oceans) have been measured for other JFC comets like Hartley 2 (Balsiger et al. 2015) and 46P/Wirtanen (Lis et al. 2019). The causes for this diversity may be already present at the formation of these comets or may result from a different evolution after their formation.
Citation: Bieler et al. 2015
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However, it is not straightforward to explain H2 formation in astronomical sources even when the catalytic roles of dust grains are introduced into models. Interstellar species are believed to be formed on cold grain surfaces via the so called Langmuir–Hinshelwood mechanism (Watson & Salpeter 1972; Pickles & Williams 1977; Hasegawa, Herbst & Leung 1992). To form H2, H atoms accrete on dust grains and then bind weakly with surfaces, which is known as physisorption. They can overcome the diffusion barrier and move on the grain surfaces via quantum tunnelling or thermal hopping. However, laboratory and theorectical studies showed that quantum tunnelling does not contribute much to the mobility of H atoms on silicate, carbonaceous or solid amorphous water (ASW) surfaces (Pirronello et al. 1997, 1999; Katz et al. 1999; Nyman 2021). If H atoms encounter other H atoms, then H2 molecules are formed. But H atoms can also desorb and leave grain surfaces. A hydrogen atom must reside on a grain long enough to find a partner H atom to form H2. As the dust temperature increases, the H atom desorption and diffusion rates also increase. So the temperature of grain surfaces must be sufficiently low so that an H atom can encounter another one before it desorbs. On the other hand, the temperature of grain surfaces must be high enough so that H atoms can diffuse on the grain surface. The parameter that measures how strongly species are to bound to grain surfaces is called desorption energy. It was found that if we assume a single H desorption energy in models, the dust temperature range over which efficient H2 formation occurs is narrow (6–10 K for olivine grains) (Katz et al. 1999). Moreover, it was found that the highest dust temperature at which H2 can be formed efficiently is less than 17 K (Katz et al. 1999). However, the grain surface temperature in the unshielded diffuse clouds, where hydrogen molecules are believed to be efficiently formed, is around 20 K (Li & Draine 2001).
Citation: Katz et al. 1999
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Citation: Katz et al. 1999
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Citation: Katz et al. 1999
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We measured the total continuum flux in the SW star-forming region (rectangular with a width of 07 in the east–west × a width of 21 in the north–south) (02h42m4059–4064, −00°00′4789–5000)J2000 to be ∼10 mJy. If this is solely due to the thermal free–free emission inside star-forming H ii-regions, then the corresponding far-infrared (40–500 μm) luminosity becomes ∼6 × 1043 (erg s−1), or there is a star formation rate of ∼3 M⊙ yr−1 (Kennicutt 1998), when Equation (1) of Nakanishi et al. (2005) is used. If dust thermal radiation contributes in some way to the continuum flux there, the estimated star formation luminosity will be smaller. Previous infrared spectroscopy failed to clearly detect the signatures of ongoing active star formation at the central few arcsec region of the NGC 1068 nucleus due to the lack of polycyclic aromatic hydrocarbon (PAH) emission features (Imanishi et al. 1997; Le Floc’h et al. 2001), which are a good probe of star formation activity (Moorwood 1986; Imanishi & Dudley 2000). It was also argued that the molecular gas properties at the NGC 1068 nucleus are dominated by AGN radiation rather than star formation (Usero et al. 2004; Garcia-Burillo et al. 2010). Thus, our continuum emission map provides a new signature for the detectable amount of star formation activity in the nuclear few arcsec region of NGC 1068, thanks to the high sensitivity of ALMA. The PAH emission flux within the nuclear 38 × 38 region in infrared spectroscopy is estimated to be 2.7 × 1040 (erg s−1) (Imanishi 2002), which corresponds to star formation-originated far-infrared luminosity with 2.7 × 1043 (erg s−1) (Mouri et al. 1990). This upper limit is apparently smaller than the above estimate but can be reconciled because (1) not all of the SW star-forming region was covered by previous infrared slit spectroscopy (Imanishi et al. 1997), (2) the estimated star formation luminosity is reduced if dust thermal radiation contributes to the observed continuum emission at ∼266 GHz, and (3) some fraction of the PAHs can potentially be destroyed by the AGN's strong X-ray radiation in the close vicinity of the AGN in NGC 1068 (Voit 1992).
Citation: Imanishi & Dudley 2000
Citation function: Compare/Contrast; Compare/Contrast
Combining different samples from various instruments at different redshifts therefore inevitably introduces ENLR size–luminosity relations with different slopes α depending on the details of target selection and analysis approaches. Slopes ranging from α = 0.22 ± 0.04 (Greene et al. 2012), α = 0.25 ± 0.02 (Liu et al. 2013b), α ∼ 0.3–0.4 (Hainline et al. 2013; Chen et al. 2019a), to α ∼ 0.5 (Bennert et al. 2002; Husemann et al. 2014) are reported in the literature. The slopes solely inferred from the CARS data are consistent with those reported by Greene et al. (2012) and Liu et al. (2013b) and are therefore on the shallower side of previous estimates. Nevertheless, the scatter in the observed relation is significant and measured slope variations might be entirely attributed to the observationally induced biases as discussed above. A slope of α = 0.5 is reminiscent of the BLR size-luminosity relation, but would require a constant ionization parameter U that demands a constant density with radius. This is not observed for the ENLR on kiloparsec scales (e.g., Bennert et al. 2006; Kakkad et al. 2018) and more detailed photoionization calculations are required to predict the shallower slopes inferred for most studies (Dempsey & Zakamska 2018). We cannot study the radial variations of U as our snapshot MUSE observations are not deep enough to map the electron density given the too low S/N of the [S II] doublet on kpc scales. However, the photoionization calculations do not take into account variable ionizing flux from AGN on 105 yr time scales (Schawinski et al. 2015) and the various geometrical intersections of the ionizing radiation field with the gas distribution of the galaxies. The CARS survey is least biased with regard to RENLR given the narrow redshift range and large dynamic range offered by MUSE (see Fig. 13). Therefore, the CARS survey is one of the best data set to explore the origin of the significant scatter in ENLR size–luminosity relation and search for additional factors or more fundamental parameters controlling the ENLR size.
Citation: Liu et al. (2013b)
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Citation: Liu et al. 2013b
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Our results have immediate stimulations for further research. First, although here we have studied the SNe Ic-BL not associated with GRBs, the main conclusion can be equally applied to GRB-SNe. It is usually believed that the central engines of GRBs are black holes (Popham et al. 1999; Narayan et al. 2001; Kohri & Mineshige 2002; Liu et al. 2007; Song et al. 2016) or magnetars (Usov 1992; Dai & Lu 1998a, 1998b; Zhang & Dai 2008, 2009, 2010; Giacomazzo & Perna 2013; Giacomazzo et al. 2015). However, since it is currently infeasible to identify the GRB central engine directly because of the cosmological distance scales of GRBs (Kumar & Zhang 2015), the researchers instead pursue indirect signatures of black holes (Geng et al. 2013; Wu et al. 2013; Yu et al. 2015) and magnetars (Dai et al. 2006; Gao et al. 2013; Wang & Dai 2013; Yu et al. 2013; Zhang 2013; Metzger & Piro 2014; Wang et al. 2015a, 2016a; Li & Yu 2016; Liu et al. 2016) that power the energetic GRBs. Growing indirect observational evidence suggests that magnetars could act as the central engines of both LGRBs and short-duration GRBs (Dai et al. 2006; Rowlinson et al. 2010, 2013; Dai & Liu 2012; Wang & Dai 2013; Wu et al. 2014; Gao et al. 2015; Greiner et al. 2015). However, because of the high mass of 56Ni needed to heat the GRB-SNe, magnetars are doubted as the candidate central engine of GRBs. With our demonstration that this high mass of 56Ni is actually not the case, such a concern is removed.8

8
We note that Cano et al. (2016a) drew the conclusion that GRB-SNe are powered by 56Ni decay under the assumption that the central engine of GRB-SNe is a magnetar. In drawing this conclusion, Cano et al. (2016a) assume that the mangetar’s rotational energy is equally divided between GRB afterglow and SNe. This assumption is somewhat ad hoc and unjustified. At the least, although the jet launching could be the result of the magnetar spin-down, it seems more likely to be the result of accretion onto the magnetar (Zhang & Dai 2008, 2009, 2010). Furthermore, Cano et al. (2016a) do not consider the origin of the kinetic energy of the GRB-SNe in their model. If we accept the assumption that a huge amount of kinetic energy of the GRB-SNe comes from the rotational energy of the magnetar, the initial rotational period of the magnetar cannot be as long as given by Cano et al. (2016a). Finally, as we mentioned above, our conclusion that the 56Ni mass for the tail modeling is usually much smaller than the peak modeling is consistent with the finding by Maeda et al. (2003).
Citation: Dai & Liu 2012
Citation function: Future Work
Despite the consensus on the importance of galactic winds in dwarf galaxy formation, large uncertainties persist in how the winds are driven. As the ISM physics is typically not resolved in full cosmological simulations (although some simulations are getting close, see e.g., Wheeler et al. 2019; Agertz et al. 2020), subgrid feedback models are often used to alleviate the numerical ‘overcooling’ problem, whereby energy injected into the ISM is rapidly cooled away because of unresolved ISM structures. Many studies have investigated supernova (SN) feedback, perhaps the most important process to drive galactic wind in dwarf galaxies. Different approaches of subgrid models were explored, ranging from kinetic feedback models where wind mass loading and velocities are predetermined (Springel & Hernquist 2003; Oppenheimer & Davé 2006; Dalla Vecchia & Schaye 2008), to explicit models where radiative cooling is temporarily shut off (Stinson et al. 2006; Agertz et al. 2011; Teyssier et al. 2013), to mechanical feedback where the momentum boost during the unresolved adiabatic phase is calibrated through small-scale ISM simulations and injected into the ISM (Hopkins et al. 2014; Kimm & Cen 2014; Smith et al. 2018). Simulations of dwarf galaxies in recent years have often adopted one of these approaches for SN feedback. Although many are considered successful in reproducing observations, few attempts have been made to compare these models in full cosmological simulations. It is therefore important to gauge how the evolution of simulated dwarf galaxies (especially at the low mass end) is dependent on feedback models, in order to provide a more robust model with realistic uncertainties for future observations. Moreover, most existing SN feedback models take into account individual SN explosions. However, recent studies such as Nath & Shchekinov (2013) and Sharma et al. (2014), have pointed out that stars form in clusters and, through multiple repeated SN explosions, generate the so-called superbubbles, which contain hot gas, and the sharp temperature gradients between hot and cold gas make thermal conduction an extremely important process. The evolution of superbubbles and the importance of thermal conduction have been investigated using ISM simulations with direct treatments of thermal conduction (e.g., El-Badry et al. 2019; Steinwandel et al. 2020). However, the resolution requirement for directly modelling conduction is still unattainable for cosmological galaxy formation simulations.
Citation: Hopkins et al. 2014
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