The difficulty resides in the definition of ‘group’ itself. If on the simulation side, groups are well defined thanks to an access to the entire three-dimensional information, on the observational side, calling an ensemble of galaxies a group constitutes a great challenge because of a restricted access to the information. In observations, knowing precisely the fraction of collapsed material becomes quite problematic. Still several schemes have been developed to define groups within galaxy catalogues. They mainly invoke Friends of Friends (FoF) like algorithms based on projected separation, radial velocities and even luminosities to identify what are called ‘groups’ of galaxies (e.g. Huchra & Geller 1982; Geller & Huchra 1983; Ramella et al. 2002; Eke et al. 2004; Yang et al. 2005; Crook et al. 2007; Lavaux & Hudson 2011; Makarov & Karachentsev 2011; Old et al. 2014; Tempel et al. 2014; Old et al. 2015). This paper does not aim at scrutinizing in detail the methods used to group catalogues. It aims at testing two recently released versions of groups for galaxies in the local Universe to understand the differences in the reconstructions generated by two various grouping schemes as described below:
Tempel et al. (2016) introduced a new grouping method (hereafter Tempel Grouping scheme). This method is based on a widely used FoF percolation method, where different linking lengths in radial (along the line of sight) and in transversal (in the plane of the sky) directions are used but the conventional FoF groups are refined using multimodality analysis. More precisely, Tempel et al. (2016) use a model-based clustering analysis to check the multimodality of groups found by the FoF algorithm and they separate nearby/merging systems. In the current paper, we use published catalogues of groups detected using this new method.Tully Grouping scheme is based on literature groups and in that respect is not a systematic scheme. Within 30 Mpc, groups are those identified by Tully (1987), further away groups are those given in the literature like Abell’s catalogue (Abell, Corwin & Olowin 1989). Recently, Tully (2015a,b) published a more systematic way of deriving groups based on radii of second turn around and iterations. After comparisons, we find that the catalogue grouped with this last scheme is an intermediate between the catalogues obtained with Tully and Tempel Grouping schemes and as such will result in more mitigated conclusions would we compare it to Tempel Grouping scheme. In addition, Tully Grouping scheme has been used so far with the second catalogue to build constrained initial conditions. We thus stick to Tully Grouping scheme in the rest of the paper.4


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).


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.


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.


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.


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.


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).


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).


Recent detections of gravitational waves (GWs) have shown evidence for a high rate of black hole (BH)–BH and neutron star (NS)–NS mergers in the universe (Abbott et al. 2016a, 2016b, 2017a, 2017b, 2017c, 2019a; Zackay et al. 2019, 2020; Venumadhav et al. 2020). However, the proposed astrophysical pathways to mergers remain highly debated. Possible compact-object merger pathways include isolated binary evolution (Dominik et al. 2012; Kinugawa et al. 2014; Belczynski et al. 2016, 2017; Breivik et al. 2016; Giacobbo et al. 2018; Bavera et al. 2019; Spera et al. 2019) accompanied by mass transfer (Inayoshi et al. 2017a; Pavlovskii et al. 2017; van den Heuvel et al. 2017), common-envelope ejection (e.g., Paczynski 1976; Ivanova et al. 2013), envelope expansion (Tagawa et al. 2018), or chemically homogeneous evolution in a tidally distorted binary (de Mink & Mandel 2016; Mandel & de Mink 2016; Marchant et al. 2016), evolution of triple or quadruple systems (Antonini et al. 2017; Liu & Lai 2017, 2018, 2019; Silsbee & Tremaine 2017; Arca-Sedda et al. 2018; Hoang et al. 2018b; Randall & Xianyu 2018; Fragione & Kocsis 2019; Fragione et al. 2019; Michaely & Perets 2019), gravitational capture (O’Leary et al. 2009; Kocsis & Levin 2012; Gondán et al. 2018b; Rodriguez et al. 2018; Rasskazov & Kocsis 2019; Zevin et al. 2019; Samsing et al. 2020), dynamical evolution in open clusters (Banerjee 2017, 2018a, 2018b; Bouffanais et al. 2019; Kumamoto et al. 2019; Rastello et al. 2019) and dense star clusters (e.g., Portegies Zwart & McMillan 2000; O’Leary et al. 2006, 2016; Samsing et al. 2014; Ziosi et al. 2014; Mapelli 2016; Rodriguez et al. 2016a, 2016b; Askar et al. 2017; Fujii et al. 2017; di Carlo et al. 2019; Zevin et al. 2019; Zhang et al. 2019), and dynamical interaction in gas-rich nuclear regions (McKernan et al. 2012, 2014, 2018; Bellovary et al. 2016; Bartos et al. 2017; Stone et al. 2017; Leigh et al. 2018; Tagawa & Umemura 2018; Yi et al. 2018; Secunda et al. 2019; Yang et al. 2019a, 2019b; Gayathri et al. 2020; McKernan et al. 2020; Tagawa et al. 2020).


Pulsar timing experiments (Sazhin 1978; Detweiler 1979) allow us to explore the low-frequency (∼1–100 nHz) part of the gravitational-wave (GW) spectrum. By measuring deviations from the expected arrival times of radio pulses from an array of millisecond pulsars, we can search for a variety of GW signals and their sources. The most promising sources in the nanohertz part of the GW spectrum are supermassive binary black holes (SMBHBs) that form via the mergers of massive galaxies. Orbiting SMBHBs produce a stochastic GW background (GWB; Lommen & Backer 2001; Jaffe & Backer 2003; Volonteri et al. 2003; Wyithe & Loeb 2003; Enoki et al. 2004; Sesana et al. 2008; McWilliams et al. 2012; Sesana 2013; Ravi et al. 2015; Rosado et al. 2015; Kelley et al. 2016; Sesana et al. 2016; Dvorkin & Barausse 2017; Kelley et al. 2017; Bonetti et al. 2018; Ryu et al. 2018), individual periodic signals or continuous waves (CWs; Sesana et al. 2009; Sesana & Vecchio 2010; Mingarelli et al. 2012; Roedig & Sesana 2012; Ravi et al. 2012, 2015; Rosado et al. 2015; Schutz & Ma 2016; Mingarelli et al. 2017; Kelley et al. 2018), and transient GW bursts (van Haasteren & Levin 2010; Cordes & Jenet 2012; Ravi et al. 2015; Madison et al. 2017; Islo et al. 2019; Bécsy & Cornish 2021). We expect to detect the GWB first, followed by detection of individual SMBHBs (Siemens et al. 2013; Rosado et al. 2015; Taylor et al. 2016; Mingarelli et al. 2017) that stand out above the GWB. Detection of GWs from SMBHBs will yield insights into galaxy mergers and evolution not possible through any other means. Other potential sources in the nanohertz band include cosmic strings (Damour & Vilenkin 2000, 2001; Berezinsky et al. 2004; Damour & Vilenkin 2005; Siemens et al. 2006, 2007; Ölmez et al. 2010; Sanidas et al. 2013; Blanco-Pillado et al. 2018; Chang & Cui 2021; Ghayour et al. 2021; Gorghetto et al. 2021; Wu et al. 2021a; Blanco-Pillado et al. 2021; Lin 2021; Chiang & Lu 2021; Lazarides et al. 2021; Chakrabortty et al. 2021; Ellis & Lewicki 2021), phase transitions in the early universe (Witten 1984; Caprini et al. 2010; Addazi et al. 2021; Arzoumanian et al. 2021; Di Bari et al.2021; Borah et al. 2021; Nakai et al. 2021; Brandenburg et al.2021; Neronov et al. 2021), and relic GWs from inflation (Starobinskiǐ 1979; Allen 1988; Lazarides et al. 2021; Ashoorioon et al. 2021; Yi & Zhu 2021; Li et al. 2021; Poletti 2021; Vagnozzi 2021; Sharma 2021), all of which would provide unique insights into high-energy and early-universe physics.


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.


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).


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.


SMBHs and host galaxies share several properties. Both SMBHs and galaxies have exponential cut-offs at the high mass end of their co-moving space densities (e.g., Shankar et al. 2009; Ilbert et al. 2013; Kelly & Shen 2013). The population of both SMBHs and galaxies also exhibit mass downsizing whereby the oldest, in the case of galaxies, and the most massive of SMBHs grew early and rapidly (e.g., Thomas et al. 2005, 2010; Merloni & Heinz 2008). However, there is a mismatch in both the shape and co-moving number density between galaxies and dark matter halos, especially at the low and high mass ends of these functions (Benson et al. 2003). Because powerful AGN can have a mechanical and radiative energy output similar to or exceeding that of the binding energy of a massive galaxy and dark matter halo, AGN are thought to play a key role in regulating the baryonic growth of galaxies. Both observations and simulations have suggested that there may be a positive trend between the mean black hole accretion rate and star-formation rate (SFR; e.g., Delvecchio et al. 2015; McAlpine et al. 2017), while the mean SFR as a function of black hole accretion rate shows no correlation for low luminosity sources (e.g., Stanley et al. 2015; McAlpine et al. 2017). One should be cautious when interpreting both theoretical and observational results in the definition of what exactly AGN feedback is and how AGN affect their host galaxies to explain the properties of an ensemble of galaxies (Scholtz et al. 2018). The strength and nature of AGN feedback – the cycle whereby the SMBH regulates both its own growth and that of its host – depends on galaxy mass and morphology. For example, the most massive elliptical galaxies are generally metal-rich and old, while less massive lenticular galaxies, which make up the bulk of the early-type galaxy population, have star formation histories that lasts significantly longer (Thomas et al. 2005, 2010; Emsellem et al. 2011; Krajnović et al. 2011). Clearly, if AGN feedback plays a crucial role in shaping the ensemble of galaxies, its impact on massive dispersion dominated galaxies must result in somewhat different characteristics in these galaxies compared to rotationally-dominated and predominately less massive lenticular galaxies.


Current observations of the interstellar neutral helium trajectories by the Interstellar Boundary EXplorer (IBEX) (McComas et al. 2009) determined the inflow longitude of the interstellar wind to be

 (Leonard et al. 2015), ∼757 (McComas et al. 2015),

 (Bzowski et al. 2015), and

 (Schwadron et al. 2015). Recent studies using the Ulysses/GAS instrument made observations of the interstellar helium distribution by detecting the sputtered ions from a lithium fluoride coated surface, finding the inflow direction to be

 (Bzowski et al. 2014) and

 (Wood et al. 2015). However, recent observations of the pickup helium focusing cone using the Plasma and Suprathermal Ion Composition (PLASTIC; Galvin et al. 2008) on board the Solar and Terrestrial Relations Observatory Ahead (STEREO A) spacecraft measured the peak density and determined the inflow longitude to be

 (Drews et al. 2012). In addition, Gershman et al. (2013) measured the peak density of the pickup helium focusing cone using the Fast Imaging Plasma Spectrometer (FIPS; Andrews et al. 2007) instrument on board the MErcury, Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft and the Solar Wind Ion Composition Spectrometer (Gloeckler et al. 1998) instrument on board the Advanced Composition Explorer spacecraft and determined inflow longitudes of

 and

, respectively. Although these pickup ion measurements fall within the 1σ uncertainties of the IBEX observations, they appear to show consistently small deviations in the same direction. Refer to Figure 1 for a diagram of the

 longitudinal difference between the observations focused on in this study of Drews et al. (2012) and Schwadron et al. (2015). The difference between the pickup helium and neutral helium observations suggests that the transport of pickup ions inside 1 au may play a role—a concept that was addressed but not factored into the derived peak longitudes in the previously mentioned pickup ion studies and has recently been up for debate (Chalov 2014; Lallement & Bertaux 2014; Frisch et al. 2015).


In analogy to the hydrodynamic case, the nonlinear coherent vortex structure also plays an important role in plasma dynamics and transport processes (Hasegawa & Mima 1978; Shukla et al. 1985; Petviashvili & Pokhotelov 1992; Horton & Hasegawa 1994). These vortices tend to have a long lifetime and are widely observed in space, laboratory, and numerical simulation of plasma (Chmyrev et al. 1988; Burlaga 1990; Volwerk et al. 1996; Stasiewicz et al. 2000; Sundkvist et al. 2005; Alexandrova et al. 2006; Alexandrova 2008; Alexandrova & Saur 2008; Vianello et al. 2010; Servidio et al. 2015). An essential subset of these plasma vortices is known as Alfvén vortices, which can be viewed as the cylindrical analog of the nonlinear Alfvén wave (Petviashvili & Pokhotelov 1992). The Alfvén vortices have an axis that is nearly parallel to the unperturbed magnetic field, along which the shape is generally invariant. Thus, these vortices are quasi-2D structures. The associated perpendicular magnetic fluctuations are linearly related with the perpendicular velocity fluctuations, but their relative amplitudes are not obligatorily equal (as is the case in an Alfvén wave):

, where ξ is not necessarily equal to 1. In addition, Alfvén vortices do not propagate along

 in the plasma frame, and they hardly propagate in the perpendicular plane when the axis of the vortex is inclined with respect to

 that are in contrast with Alfvén wave (Wang et al. 2012). After first being reported in the Earth’s magnetosheath (Alexandrova et al. 2006; Alexandrova 2008), multiscale quasi-bidimensional Alfvén vortices (with

) have been identified in numerous space environments: in slow solar wind (Perrone et al. 2016; Roberts et al. 2016), in fast solar wind (Lion et al. 2016; Perrone et al. 2017), and in Saturn’s magnetosheath (Alexandrova & Saur 2008). It seems that the intermittent structures in fast solar wind are dominated by Alfvén vortices (Perrone et al. 2017), which agrees with the 2D MHD turbulence model (Zank et al. 2017).


The connection between long-duration GRBs (LGRBs) and SNe was predicted theoretically (Colgate 1974; Woosley 1993) and has been verified observationally (e.g., Galama et al. 1998; Hjorth et al. 2003; see a review in Woosley & Bloom 2006). They usually happen in the star formation regions of the galaxies (e.g., Paczyński 1998; see the reviews in Woosley & Bloom 2006 and Kumar & Zhang 2015). The immensely bright afterglows illuminate the gas and dust within the star-forming regions of the host galaxy and intervening intergalactic medium along the GRB line of sight. Their spectra are usually featureless power-laws or broken power-laws, which can be well described by the synchrotron radiations of relativistic electrons. Therefore, GRB afterglows are good probes of burst environment and the interstellar dust and gas in distant, star-forming galaxies (Metzger et al. 1997; Jensen et al. 2001; Savaglio et al. 2003; Vreeswijk et al. 2004; Chen et al. 2005; Prochaska et al. 2007a; Schady et al. 2007, 2010; Watson et al. 2007; Fox et al. 2008; Starling et al. 2008; Jang et al. 2011; Xin et al. 2011). GRB afterglow spectra with Lyα absorption features indicate the presence of large column densities of cold neutral gas within GRB host galaxies, and their hydrogen column densities (

) are usually larger than

 cm−2 (e.g., Prochaska et al. 2007b; Schady 2012). The damped Lyα systems (DLA) may represent the ISM near the GRBs in a few kiloparsecs (Kpc), but not gas directly local to the GRB (Prochaska et al. 2007b). Thus GRB optical afterglows may be used as probes of the ISM in their host galaxies, as the ISM observed is less affected by the GRB or its progenitor (Watson et al. 2007). The visual dust extinctions (

) along the GRB lines of sight of many GRBs are low. As shown in Greiner et al. (2011), about 50% of GRBs observed with GROND after the launch of Swift mission have

. In addition, the early optical light curves of about one-third of GRBs show a smooth onset bump (Li et al. 2012). It may be due to the deceleration of the GRB fireball by the ambient medium (Sari & Piran 1999; Kobayashi & Zhang 2007). In this scenario, the rising slope of the bump is determined by the medium density profile (

) and the spectrum index of the accelerated electrons

], says,

 (Liang et al. 2013). Hence, The afterglow onset bumps would be also an ideal probe to study the properties of the fireball and the profile of the circumburst medium. Liang et al. (2013) found that

 (see also Watson et al. 2007; Jin et al. 2012).


In this paper, we performed statistical analyses on the chirality and the magnetic configurations (inverse-polarity versus normal polarity) of the solar filaments that erupted on the solar disk from 2010 May 13 to 2015 December 31 covering both the rising phase and the beginning of the declining phases of solar cycle 24. The chirality is determined by an indirect method proposed by Chen et al. (2014), i.e., left-/right-skewed drainage corresponds to dextral/sinistral chirality. The determination of the magnetic configuration is also based on a method proposed by Chen et al. (2014), i.e., those filaments that follow Martin's Rule (Martin et al. 1994) are of the inverse-polarity type, and those that disobey Martin's Rule are of the normal-polarity type. By studying a sample of 571 filaments, we obtained the following results.

(1)
About 94.8% of the filaments in the northern hemisphere have negative helicity, and 87.4% of the filaments in the southern hemisphere have positive helicity, indicating a significant hemispheric preference of helicity. As a whole, 91.6% of our sample of erupting filaments follows the hemispheric rule of helicity sign. With the improved method for determining the filament chirality, the strength of the hemispheric rule is higher than that in previous studies. It should be noted that the statistical result is based on the erupting filaments. Those filaments that do not erupt during the disk passage are not included in our sample.

(2)
Following convention, we divided the filaments into three types, the quiescent type, the intermediate type, and the active-region type. It is shown that the strength of the hemispheric rule is 93% for the quiescent filaments, 95% for the intermediate filaments, and 83% for the active-region filaments.

(3)
Regarding the cyclic behavior of the hemispheric preference, it is found that the strength of the quiescent filaments decreases slightly from ∼97% in the rising phase to ∼85% in the declining phase, whereas the strength of the intermediate filaments keeps a high value around 96 ± 4%. Only the active-region filaments show significant variations. Their strength of the hemispheric rule rises from ∼63% to ∼95% in the rising phase, and keeps a high value of 82% ± 5% during the declining phase. However, during a half-year period around the solar maximum, the hemispheric preference totally vanishes.

(4)
It is found that in our sample of erupting filaments, 89% are inverse-polarity filaments that are magnetically supported by a flux rope, whereas 11% are normal-polarity filaments that are magnetically supported by a sheared arcade.


One of the major endeavors of modern observational cosmology is to paint a coherent picture of the history of the universe. To this end, the final frontier remains the identification and characterization of the first sources that appeared in the universe, those which played a significant role in reionizing the intergalactic medium from a neutral state to a fully ionized one over the first billion years (corresponding to redshifts of 6 ≲ z ≲ 12). Extragalactic surveys (of deep fields as well as lensing clusters; Grogin et al. 2011; Koekemoer et al. 2011; Bradley et al. 2012; Ellis et al. 2013; Bradley et al. 2014; Schmidt et al. 2014; Treu et al. 2015; Lotz et al. 2017; Salmon et al. 2018; Coe et al. 2019) with the Hubble Space Telescope (HST) have yielded impressive gains in the number of galaxy candidates at redshifts z = 7–10, with samples reaching over 1000 objects, and revolutionized our understanding of galaxy evolution therein. Complementing these observations, the spectroscopic confirmation (e.g., Finkelstein et al. 2013; Oesch et al. 2015; Zitrin et al. 2015; Roberts-Borsani et al. 2016; Hoag et al. 2017; Stark et al. 2017; Hashimoto et al. 2018) and characterization (e.g., Laporte et al. 2017; Mainali et al. 2018; Endsley et al. 2021) of over a dozen sources has seen impressive advances with ground-based spectroscopy (e.g., probing the rest-frame UV and FIR with Keck/MOSFIRE, VLT/X-Shooter, and ALMA), particularly for the brightest and rarest objects. For the rest-frame optical, however, the Spitzer Space Telescope has, until now, afforded the only realistic means for statistical analyses. However, the Infrared Array Camera’s (IRAC) coarse spatial resolution and the limited depth probed by many surveys make robust and uncontaminated constraints on galaxy properties a challenging feat. Further advances with current facilities are challenging owing to the limited wavelength coverage of HST and the observed faintness of star-forming galaxies as one approaches redshifts of z > 10. The imminent arrival of the James Webb Space Telescope (JWST) has the potential to detect galaxies well beyond the current frontier of z ∼ 12 (e.g., Behroozi et al. 2020) thanks to the unprecedented resolution and sensitivity of its near-IR (NIR) imaging and spectroscopic capabilities, and revolutionize our current understanding of galaxy evolution.


The SFG population bias factor, bSFG may be calculated from the ratio of galaxy to dark matter correlation functions, i.e:
(27)$$\begin{eqnarray*}
b_{\rm SFG}^2(z) &=& \frac{\xi _{\rm g}(r, z)}{\xi _{\rm DM}(r, z)} \nonumber \\
&=& \left(\frac{r_0(z)}{8} \right)^{\iota } \, \frac{J_2}{\sigma _8^2 \,\, \mathcal {G}^2(z)}
\end{eqnarray*}$$(e.g. Kaiser 1984; Bardeen et al. 1986; Lindsay et al. 2014), where the matter fluctuation amplitude σ8 = 0.811 ± 0.006 (Planck Collaboration 2020), and $\mathcal {G}(z) = g(z)/g_0$, with g(z) as the growth factor at redshift z and g0 = g(z = 0) (e.g. Carroll, Press & Turner 1992).7 Additionally, J2 = 72/([3 − ι][4 − ι][6 − ι]2ι), and $r_0(z) = r^{\rm c}_0 (1+z)^{p}$ with p = 1 − (3 + w)/ι (Lindsay et al. 2014). Here, the choice of the parameter w reflects the clustering model adopted. In this demonstrative model, we consider only linear clustering (Overzier et al. 2003) where clustering growth is set by linear perturbation theory and w = ι − 1. We leave the investigation of alternative clustering growth models to future work – for example, stable clustering (where clusters have a fixed physical size and w = 0), co-moving clustering (where clusters have fixed co-moving size and w = ι − 3) and decaying clustering (which implies a rapid clustering decay) are also considered in the literature (Overzier et al. 2003; Kim et al. 2011; Elyiv et al. 2012). The remaining parameters in equation (27) are the power-law slope of the two-point correlation function of galaxies, ι, and the galaxy clustering length $r_0^{\rm c}$. Both of these may be estimated empirically for SFGs, and we adopt the best-fitting values of Hale et al. (2018): ι = 1.8 and $r_0^{\rm c} = 6.1\, \text{Mpc}\, h^{-1}$. These were computed from radio-selected SFGs in the COSMOS field using deep Karl G. Jansky Very Large Array (VLA) data at 3 GHz, reaching redshifts as high as z ∼ 5, thus covering our range of interest (z ≤ 3).8 The resulting bias factor from these parameter choices is higher than those computed for SFGs at other wavelengths (e.g. Gilli et al. 2007; Starikova et al. 2012; Magliocchetti et al. 2013), but this is attributed to the greater extent of the redshift distribution of the sources.


In Fig. B.1 we plot the soft X-ray light curves for our candidate highly variable AGN using available X-ray data taken by the satellite missions Einstein, ROSAT, XMM, Suzaku and Swift. The count rates were obtained from different archives including HEASARC, the XMM Science Archive, the Swift UKSSDC and from our own Swift XRT data analysis, and for upper limits the 1SXPS catalogue (Evans et al. 2014) and the XMM upper limit server2 were queried. The count rates of the different satellites were converted to fluxes between 0.2–2.0 keV using PIMMS3 assuming a power law with a photon index of 1.7 as a spectral shape taking into account Galactic extinction as given by Willingale et al. (2013). Sobolewska & Papadakis (2009) found a positive correlation between flux and spectral slope for a sample of bright RXTE AGN in the 2–10 keV band. This could affect the relative fluxes seen in our sample which are plotted in Fig. B.1. We have attempted to quantify this for the different detectors used in the creation of our light curves. The sample of Sobolewska & Papadakis (2009) showed spectral changes with observed power-law slope varying between 1.0 and 2.0 (see their Fig. 7). For a typical Galactic absorption of 3 × 1020 cm-2 the change from slope of 1.0 to 2.0 would alter our estimated fluxes by −14% (ROSAT), −13% (XMM-Newton), +7% (Swift-XRT), +76% (Suzaku), +25% (Einstein-IPC). The change is large for Suzaku observations since in this case we use the count rate between 2.0–10.0 keV and extrapolate it to the soft band. All other satellites are sensitive in the soft band and hence the fluxes are less dependent upon the assumed spectral index. Six sources within our sample (XMMSL1 J024916.6-041244, J034555.1-355959, J045740.0-503053, J051935.5-323928, J070841.3-493305, and J193439.3+490922) display variation in flux of a factor of ten or greater between at least one pair of XMM and Swift observations, on timescales of months to years. The ratio between the soft X-ray flux observed with Swift and that observed with XMM for the remaining sources is typically a factor of a few. We observed the two TDE candidates with XRT, and found that both had faded significantly, following expectations from previous and later fluxes and upper limits (Figs. 1p and h).


Traditionally, the Parker transport equation (Parker 1965) was used to model pickup ion acceleration at the SWTS when using a transport theory approach. However, interesting Voyager results, such as strongly fluctuating pitch-angle anisotropies upstream, the detection of highly anisotropic intensity spikes at the SWTS, the average upstream anisotropy peaking at a surprisingly high energy far above the upstream flow energy, and energetic particle spectra with multiple power-law slopes with breaking points in between that are highly variable upstream (Decker et al. 2005, 2008b; Cummings et al. 2006), suggest that transport modeling should be modified in at least two ways. First, the turbulent nature of the magnetic field conditions at the SWTS should be taken into account, and second, a transport theory that applies when particle distributions are highly anisotropic is needed, given that the Parker transport equation only holds for nearly isotropic particle distributions. In response, shock acceleration transport models were developed in recent years based on the numerical solutions of the focused transport equation (Kóta & Jokipii 2004; le Roux et al. 2007; Florinski et al. 2008a, 2008b; le Roux & Webb 2009) to take advantage of the fact that focused transport is not restricted to small pitch-angle anisotropies. This is especially advantageous at lower suprathermal particle energies upstream, where particle distributions can be sporadically strongly anisotropic (Decker et al. 2006), allowing one to model particle injection into diffusive shock acceleration (DSA) naturally at those energies. Furthermore, statistical variations in the observed magnetic field direction near the SWTS were included as a time series to model time variations in the injection and DSA of pickup ions to simulate the highly volatile nature of actual DSA at the SWTS (Florinski et al. 2008a, 2008b; le Roux & Webb 2009; Arthur & le Roux 2013). This focused transport approach should be seen as complementary to more sophisticated self-consistent shock acceleration models based on hybrid codes (Kucharek & Scholer 1995; Giacalone 2005) and particle-in-the-cell models (Scholer et al. 2003; Lembege et al. 2004), but it has the virtue of relative simplicity because different statistical plasma parameters can easily be studied separately and in combination at the SWTS to gain a more clear conceptional understanding of the role of such statistics on pickup acceleration at the SWTS.


Thereafter, we proceeded to fit the broad-band spectrum with the analytical model ‘COMPMAG’ and its updated version. The ‘COMPMAG’ model has many spectral parameters that were impossible to constrain simultaneously. Especially, β if left free preferred a value >0.9 in the fit, which is greater than the maximum possible terminal velocity at the neutron star surface. Therefore, we tested the two-component ‘COMPMAG’ model, assuming reasonable parameter values applicable to low-luminosity accretion-powered pulsars, i.e. free-fall velocity profile (η = 0.5), and β = 0.5 that corresponds to the maximum terminal velocity at the neutron star surface, and a pencil beam emission pattern. The radius of the accretion column (r0) was set to 0.25 corresponding to a radius of ∼1 km for a neutron star of mass 1.4 M⊙. The Albedo at the neutron star surface (A) was set to 1. This provided an acceptable fit to the data with acceptable and physically viable parameter values. Addition of a CRSF feature was not required for this model. The high β preferred by the fit indicated that bulk motion Comptonization (BMC) dominates the Comptonization process in X Persei. The fit also provided a low value of electron temperature (kT) consistent with the high value of β obtained (Farinelli et al. 2012). The dominance of BMC in low-luminosity pulsars, especially X-Persei has been predicted before and is consistent with our results (Becker & Wolff 2005, 2007). In Becker & Wolff (2007), the authors qualitatively described the observed spectrum of X-Persei with a pure BMC model. The obtained reduced χ2 value with ‘COMPMAG’ was higher than that obtained with the ‘newhcut’ model (1.84 for 569 dof), although there were no systematic pattern in the residuals, and the higher χ2 was mainly contributed by a higher variance in a few energy bins of the XIS spectra. Fit with the updated ‘COMPMAG2’ model was difficult with the given statistical quality of the data, as it required fitting many more additional parameters. Moreover as discussed in Farinelli et al. (2016), the ‘COMPMAG’ model provides an adequate description of the spectrum for low-luminosity pulsars, where the blackbody emission provides the major source of photons for Comptonization, appropriate for the case of X Persei. Considering the above, and as ‘COMPMAG’ is a more physical model to understand the continuum spectra of the source, we used this as our best-fitting model for the rest of the paper. As a CRSF feature is not required for this model, we cannot claim the presence of a CRSF in the average spectrum of X Persei. Table 1 summarizes the best-fitting broad-band spectral parameters obtained using the ‘newhcut’ (without addition of the CRSF) and ‘COMPMAG’ models. Fig. 3 shows the best-fitting unfolded spectrum for the best-fitting model, showing the model components.


The GRMHD model library is limited in that it has only a few discrete parameter values for the magnetization, black hole spin, and electron temperature distributions. Apart from increasing the number of discrete values, one could think about ways to interpolate between these using, for example, machine learning techniques (van der Gucht et al. 2020; Yao-Yu Lin et al. 2020) or fit to semi-analytic models (e.g., Broderick et al. 2016). The variability within the GRMHD models was found to be an important limitation for constraining black hole parameters, as attested by, for example, the small difference in recovered parameters between the EHT2021 and EHT2021+ arrays. The analysis pipeline may be extended to include a characterization of the source variability as part of the model selection process (e.g., Kim et al. 2016; Roelofs et al. 2017; Medeiros et al. 2017, 2018; Johnson et al. 2017; Bouman et al. 2018; Wielgus et al. 2020), which could improve the constraining power beyond the averaging method introduced here. EHT expansions are expected to make the large-scale jet visible in reconstructed images of the black hole shadow due to an increased dynamic range (Doeleman et al. 2019; Roelofs et al. 2020; Raymond et al. 2021). This connection between event-horizon scales and the extended jet has not been taken into account in the parameter estimation framework used here, as the GRMHD library images have a small field of view (160 μas). With the development of GRMHD simulations that have the ability to connect large (e.g., Fromm et al. 2017, 2018, 2019; Liska et al. 2018; Chatterjee et al. 2019) and small scales at different wavelengths and of an extended fitting framework, the constraining power is expected to improve especially for EHT extensions and space arrays. For a mass measurement, feature extraction techniques such as a ring fit (Event Horizon Telescope Collaboration 2019d,f) may be used, potentially in combination with fitting the more extended (variable) structure (Broderick et al. 2020b). Models and analysis techniques for Sgr A* and polarization could be considered as well. These possible avenues for further simulation and fitting framework development mean that the parameter constraints presented in this paper should not be interpreted as set limits on the constraining power of the considered arrays. Rather, they show what is achievable with the current state of the art.


The Λ cold dark matter(ΛCDM) paradigm (Blumenthal et al. 1984) predicted cuspy DM density profiles in the centre of galaxies (Navarro, Frenk & White 1996) and recent results with higher resolution seem to suggest a shallower DM density profile (Navarro et al. 2004, 2010). None the less, the past and newer developments are in contradiction with observational results of cored DM density profiles in dwarf galaxies (de Blok, McGaugh & Rubin 2001; de Blok & Bosma 2002; Swaters et al. 2003; Chemin, de Blok & Mamon 2011). This discrepancy is known as the cuspy-core problem. Many other authors, in an attempt to contribute to the solution of the cuspy-core problem, performed the RC decomposition considering a constant M/L (Carignan & Freeman 1985, 1988; Jobin & Carignan 1990; Persic & Salucci 1990; Martimbeau, Carignan & Roy 1994; Persic, Salucci & Stel 1996; Blais-Ouellette, Amram & Carignan 2001; de Blok, McGaugh & Rubin 2001; de Blok & Bosma 2002; Swaters et al. 2003; Kuzio de Naray et al. 2006; Fuentes-Carrera et al. 2007; Spano et al. 2008; Kuzio de Naray, McGaugh & de Blok 2008; Repetto et al. 2010; Chemin et al. 2011) and reinforce the evidence of a cored DM distribution in the central part of galaxies. Conversely, other authors pursue spectrophotometric and SPS studies to derive the M/L of the stellar component (Salucci, Yegorova & Drory 2008; de Denus-Baillargeon et al. 2013; Repetto et al. 2013) or at least to constrain the stellar component (Dutton et al. 2005) to avoid the disc–halo degeneracy. Salucci et al. (2008) accomplished RCs mass modelling and spectral energy distribution (SED) fitting with spectrophotometric models to obtain the disc masses of 18 Sa spiral galaxies (principally bulgeless systems) finding that by decomposing the RCs with the spectrophotometric disc masses the results are consistent with the corresponding maximum disc solution. Repetto et al. (2013) employed the Zibetti et al. (2009) method to optical (SDSS) and NIR (2MASS) images of NGC 5278 (KPG 390A) to obtain the stellar disc mass profile of that galaxy from SPS models to reproduce the RC of KPG 390A. The new strategy relied on fitting only the DM RC, obtained by subtracting the SPS baryonic disc from the observed RC of NGC 5278. The most important finding of Repetto et al. (2013) is that the favoured DM distribution is cored when the disc mass approximate to the maximum disc solution in agreement with the general belief. de Denus-Baillargeon et al. (2013) used a chemo-spectrophotometric galactic evolution model to determine the stellar M/L and perform the RC decomposition of 10 spiral and dwarf irregular galaxies from the SINGS survey (Kennicutt et al. 2003). The authors employed the settled baryonic disc as a weighting function to fit the model DM halo to the RCs of the studied subsample of galaxies, and concluded that the stellar discs obtained from their chemo-spectrophotometric models were compatible with the MDH. The few examples presented indicate that a growing effort to break the disc–halo degeneracy exists; however, the most significant contributions trying to address the cuspy-core problem still rely on the general assumption of considering the stellar M/L constant along the galactic disc. In general, it is still missing a significant endeavour to earn the DM distribution, through RC fitting, determining the disc stellar mass from SPS studies. For this reason, it is worth considering the cuspy-core problem with a different observational approach, focusing on the formulation of a general procedure to better constrain the baryonic disc mass in galaxies.


MRI is considered to be the most promising mechanism driving angular-momentum transport in protoplanetary disks (Balbus & Hawley 1991; Brandenburg et al. 1995; Hawley et al. 1995; Balbus et al. 1996; Balbus & Hawley 1998). However, protoplanetary disks are cold, dense, and, therefore, poorly ionized. The low level of ionization tends to decouple the disk gas from magnetic fields, which generates non-ideal MHD effects: Ohmic dissipation, ambipolar diffusion (AD), and the Hall effect (e.g., Armitage 2011; Turner et al. 2014). These effects quench MRI in different ways: Ohmic dissipation originates from collisions between electrons and neutrals, AD from collisions between ions and neutrals, and the Hall effect from drift between electrons and ions (Fleming et al. 2000; Sano & Stone 2002; Bai & Stone 2011). Ohmic dissipation operates in high-density regions with weak field, AD dominates in highly ionized and low-density regions, and the Hall effect lies in between (Fleming & Stone 2003; Bai & Stone 2011; Bai 2014). So far, the effect of Ohmic dissipation has been best studied. Investigations show the layered accretion in the inner disk, where the midplane region is “dead” due to low ionization while the surface layer is “active” due to sufficient ionization (Gammie 1996; Jin 1996; Fleming et al. 2000; Fleming & Stone 2003; Turner et al. 2007; Ilgner & Nelson 2008; Oishi & Mac Low 2009; Okuzumi & Hirose 2011). Recent works that take into account both Ohmic dissipation and AD show that AD may render the surface layer and portions of the outer disk inactive (Bai & Stone 2011; Landry et al. 2013; Kalyaan et al. 2015). Bai & Stone (2013) find that MRI is completely suppressed in the inner disk and a strong magnetocentrifugal wind is launched. Three-dimensional simulations that include all three non-ideal MHD effects are also performed (Bai 2014, 2015; Lesur et al. 2014; Simon et al. 2015). In the inner disk, the influence of the Hall effect on midplane angular-momentum transport depends on the orientation of the vertical magnetic field with the disk rotation axis. When the field is aligned with the axis, the enhanced Maxwell stress promotes angular-momentum transport. When the field is anti-aligned with the axis, the midplane remains quiescent. In the outer disk, the Hall effect has little influence on the disk turbulence. Although the inclusion of AD and the Hall effect substantially changes the level of turbulence in the protoplanetary disks, the feature that the viscosity is low in the inner disk and high in the outer disk is still valid. In this study, we assume that gas giant planets form in situ via the core accretion scenario, which implies that their formation locations are always in the low-viscosity region. Since in this study we focus on the relation between photoevaporation and planet formation and gap opening by planets in the disk, we adopt Ohmic dissipation to represent the non-ideal MHD effects on the MRI. We consider that this simplification has little influence on our main calculation results.


A question of relevance to life on other planets is whether the Snowball catastrophe occurs at lower radiative forcing at low or high obliquity. There is some conflict in the literature on the role of the Snowball bifurcation in planetary habitability. On the one hand, defining habitability in terms of surface liquid water suggests that a Snowball climate is uninhabitable, which has led various authors to propose metrics of fractional or seasonal habitability for planets with partial ice cover (e.g., Williams & Pollard 2003; Spiegel et al. 2008). On the other hand, not only did photosynthesis persist through Snowball events in Earth’ history, but the events may have crucially shaped the subsequent evolution of complex life (e.g., Hoffman & Schrag 2002; Hoffman et al. 2017; Laasko & Schrag 2017). The traditional habitable zone concept assumes a planet with a functioning silicate-weathering feedback and a positive CO2 greenhouse effect (Kasting et al. 1993; Kopparapu et al. 2013). Global glaciation may be triggered on such a planet through a rapid drawdown of atmospheric CO2 that reduces q below the thresholds at which the non-Snowball states disappear. In Earth history, this seems to have occurred through accidents of tectonics (Hoffman et al. 2017). These events are self-terminating through the suppression of silicate weathering and accumulation of a strong CO2 greenhouse. Such transitions can in principle occur anywhere within the habitable zone. Our simple model is unsuited to the tasks of diagnosing the inner edge of habitability (where the relevant physics are the runaway water vapor greenhouse and hydrogen loss to space) or the outer edge (where the relevant physics are CO2 condensation and Rayleigh scattering). However, we can compute the q value at which the bifurcation occurs as a function of obliquity and other model parameters, and whether the transition into the Snowball state occurs from a partially ice-covered state (cap or belt) or directly from the ice-free state.


In Fig. 5, we consider how the velocity dispersion profile scales with radius. Specifically, we plot the power-law index (η) versus the central velocity dispersion (σ0). The vast majority of the galaxies with σ0 ≲ 2.45 are BGGs and these have negative η values. This includes most of the Romulus galaxies (red filled and open circles), the L18 BGGs (blue crosses) and the early-type galaxies that comprise the SAURON sample (Cappellari et al. 2006; grey line and shaded area). In contrast, nearly all of simulated BGGs with σ0 ≲ 2.45) from the DIANOGA Hydro-10x simulations Marini et al. (2021) have positive η values. For σ0 ≳ 2.45, the spread of η for the observed galaxies (e.g. L18 and Newman et al. 2013 BCGs) broadens and spans both positive and negative η values. In fact, majority of the galaxies tend to have positive ηs. This change in behaviour is well known. A number of studies have noted that on the group-scale and lower, the stellar velocity dispersion profile of the central galaxies tend to decrease with increasing radius. On the cluster-scale, the BCGs typically have rising velocity dispersion profiles with increasing radius (Von Der Linden et al. 2007; Bender et al. 2015; Veale et al. 2017). The origin of this flip is still not well understood. We leave a more detailed investigation of this change to future work. Here, we simply mention two possible explanations: The change in slope may be a reflection of the differences in the dynamical state (e.g. mass-to-light ratio; M/L) at the outskirts of BCGs (Dressler 1979; Fisher, Illingworth & Franx 1995; Sembach & Tonry 1996; Carter et al. 1999; Kelson et al. 2002; Loubser et al. 2008; Newman et al. 2013; Schaller et al. 2015; Marini et al. 2021), or it could be due to increased contribution from the intragroup/intracluster light along the line-of-sight and the increased leverage of tangential orbits (Loubser et al. 2020). All of these effects are linked to the increased frequency of galaxy–galaxy interactions and more specifically, central-satellite interactions, implicated in the build-up of extended diffuse stellar component. And, as discussed by Schaye et al. (2015), Oppenheimer et al. (2021), and the EAGLE simulations clearly show that the extended stellar halo becomes increasingly more important, and hosts a non-trivial fraction of the total stellar mass towards the cluster scale.


The Sun, our nearest late-type star exhibits a magnetic activity cycle with a period of around 11 yr. The cyclic magnetic field is generated by a dynamo operating below the surface, where it converts the energy of rotating convective turbulence into magnetic energy. We are still far from fully understanding the solar dynamo mechanism (e.g., Ossendrijver 2003; Charbonneau 2014). One reason is the limited information about the dynamics in the solar convection zone. Helioseismology has provided us with the profile of temperature and density stratification and the differential rotation (e.g., Schou et al. 1998) in the interior. Further information, such as the meridional circulation profiles, convective velocity strength, or even magnetic field distributions, are currently inconclusive or not even possible (e.g., Basu 2016; Hanasoge et al. 2016). One way to investigate how important differential rotation, meridional circulation, and turbulent convective velocities are for the solar dynamo is to use numerical simulations. Since the early simulations by Gilman (1983), the increase of computing resources has prompted several advances using numerical simulations. Nowadays, global simulations of convective dynamos are able to reproduce cyclic magnetic fields and dynamo solutions resembling many features of the solar magnetic field evolution (Ghizaru et al. 2010; Käpylä et al. 2012; Warnecke et al. 2014; Augustson et al. 2015), even long-time evolution (Augustson et al. 2015; Käpylä et al. 2016; Beaudoin et al. 2016). The cyclic magnetic field in these simulations can be well understood in terms of Parker–Yoshimura rule (Parker 1955; Yoshimura 1975; Warnecke et al. 2014), in which a propagating αΩ dynamo wave is excited; see also Gastine et al. (2012). The α effect (Steenbeck et al. 1966) describes the magnetic field enhancement from helical turbulence and the Ω effect the shearing of magnetic field caused by differential rotation. The propagation direction of the dynamo wave depends on the sign of α and shear: to generate an equatorward propagating wave, the product of α and the radial gradient of Ω must be negative (positive) in the northern (southern) hemisphere. Explaining the solar equatorward propagation of the sunspot appearance by the Parker–Yoshimura rule therefore requires either invoking the near-surface-shear layer (Brandenburg 2005), because the radial gradient is only negative in that layer (Barekat et al. 2014) and α is positive, or changing the sign of α in the bulk of the convection zone (Duarte et al. 2016) where the radial shear is positive. Furthermore, to understand the magnetic field evolution fully in the global numerical simulation one needs suitable analysis tools to extract the important contribution of turbulent dynamo effects. One of these tools is the test-field method (Schrinner et al. 2005, 2007; Warnecke et al. 2018). This method allows the determination of the turbulent transport coefficients directly from the simulations. This includes the measurement of tensorial coefficients such as α, turbulent pumping, and turbulent diffusion. The first application to global convection simulations of solar-like dynamo has already revealed that the turbulent effects can have a significant impact on large-scale magnetic field dynamics (Warnecke et al. 2018; Gent et al. 2017).


The mechanisms of CL-AGNs are still debated. Early explanations mainly focus on the change in obscuration (Bianchi et al. 2005), while some recent studies favor the change of accretion rate (Stern et al. 2018; Sniegowska et al. 2020). Photoionization research shows that the CL behavior in CL quasars can be fully explained by the photoionization responses of the BELs to the extreme variability of the ionizing continuum (Guo et al. 2020). Besides, several objects can be interpreted as tidal disruption events (Merloni et al. 2015; Ricci et al. 2020). All of the models can generate significant variance in the observed intensity of continuum and emission lines. These models might be restricted by statistical studies after extending the sample of CL-AGNs. Several groups have been devoted to searching for new CL-AGNs, and tens of candidates have been discovered based on large optical and X-ray surveys, such as the Sloan Digital Sky Survey (e.g., LaMassa et al. 2015; MacLeod et al. 2016; Rumbaugh et al. 2018; Hon et al. 2020), intermediate Palomar Transient Factory (Gezari et al. 2017), Pan-STARRS1 (MacLeod et al. 2019), Catalina Real-time Transient Survey (Yang et al. 2018), and XMM-Newton slew survey (Zetzl et al. 2018; Kollatschny et al. 2020). In the future, the Large Synoptic Survey Telescope will regularly monitor millions of AGNs, and the number of CL-AGNs will further increase. The evolution of the AGN type can be well studied if we obtain continuous multiwavelength data of the complete changing process, and the development of time-domain surveys makes it possible to achieve this. The brightening of NGC 2617 (Shappee et al. 2014) and 1ES 1927+654 (Trakhtenbrot et al. 2019) triggered the alert of the All-Sky Automated Survey for Supernovae,7

7

http://www.astronomy.ohio-state.edu/asassn

 and follow-up observations confirmed these two CL-AGNs. However, so far, there are only small available data sets for most detected CL-AGNs, especially around the critical regime of transition between types 1 and 2. Moreover, previous studies rarely gave the change of geometry and kinematics in broad-line regions (BLRs).


The level of solar activity is typically represented by the number or area of sunspots. On the other hand, the “rush-to-the-pole” behavior of PCFs is believed to be one of the precursors for the next solar maximum (Altrock 2014). Figure 3 shows the maximum sunspot numbers (SILSO World Data Center 1914–2020) in comparison with the migration speed of PCFs (orange dots) in the northern and southern hemisphere for cycles 16–24. Within these nine solar cycles, the maximum sunspot number varies in two large periods, cycles 16–20 and 20–24. Note that even in longer time spans, such as from cycle 1 to 24, the sunspot number seems to vary periodically in different periods, like the often-discussed Gleissberg cycle (Le Mouël et al. 2017). The migration speeds of PCFs in the northern hemisphere can be separated into two groups, before and after cycle 20, which is similar to the trend found in the variation of sunspot numbers. However, the migration speed in the southern hemisphere is not correlated with the sunspot number and the migration speed in the northern hemisphere. As shown in Table 2, the Pearson correlation coefficients are below 0.5 in both hemispheres, in which the value for the northern hemisphere is relatively higher. To investigate the trend of temporal variation, the derivatives of migration speeds and sunspot number are calculated. The correlation between these derivatives represents the relationship between the temporal trends. It shows that the periodical variation of PCF migration in the northern hemisphere correlates better with the variation of sunspot numbers, but the absolute values of coefficients are not high (∼0.5). Based on this result, a causal connection between the temporal variations of PCF migration and the maximum number of sunspots cannot be established. In the SILSO World Data Center (1914–2020) archive of sunspot numbers, clear NS asymmetry after cycle 20 is noticed. But the overall levels of the sunspot numbers are the same in the northern and southern hemispheres. Therefore, the NS asymmetry, which is relatively small compared to the absolute sunspot number, will not affect the results shown in Figure 3.


Our analysis on the LAMOST-RC stars by dissecting the MAPs shows that the chemical bimodality is observed throughout the Galactic disk, and the high- and low-[α/Fe] sequences are corresponding to the thick and thin disks of the Milky Way, respectively. How to explain the formation mechanism of the stellar thin and thick disks is beyond the scope of this paper, but our results provide some observational constraints to the model of the chemodynamical evolution of the Milky Way disk. Our flared vertical profiles for the thin and thick disks are in good agreement with the prediction of the thin+thick flaring disk model (López-Corredoira & Molgó 2014), and are consistent with the number simulations of the chemodynamical evolution in Galactic disks formed in the cosmological context (Minchev et al. 2013, 2014, 2015, 2017), as well as the cosmological zoom simulation of VINTERGATAN (Agertz et al. 2021). These model simulations suggest that the vertical flaring trends are a natural consequence of inside-out, upside down growth coupled with disk flaring (see also Bird et al. 2013; García de la Cruz et al. 2021), which allows for the low-[α/Fe] stars to exist several kpc above the disk’s midplane. As analyzed by B16, the exponential flaring profiles for the low-[α/Fe] MAPs suggests that radial migration played an important role in the formation and evolution of the thin disk. Radial migration of stars via cold torquing, also known as “churning,” by a bar and spiral waves (Minchev et al. 2013) then allows for the populations to spatially overlap in the solar neighborhood. Similar to the flared thin disk, the flaring profile for the high-[α/Fe] MAP indicates the radial migration has occurred in the formation of the thick disk as suggested by model simulations (e.g., Schönrich & Binney 2009; Minchev et al. 2015; Li et al. 2018). Of course, we cannot rule out the other formation scenarios of the thick disk, such as the accreted gas from satellites (Brook et al. 2004), accreted stars from galaxy mergers (Abadi et al. 2003), or from disk-crossing satellites heating up the thin disk (Read et al. 2008). On the other hand, the broken exponential radial profiles for the thin and thick disks cannot be explained by any model of the galactic disks. In fact, nearly all the models we mentioned above present a single-exponential profile decreasing with the increasing of R (e.g., Minchev et al. 2015; Li et al. 2018; Agertz et al. 2021). And the smooth downtrend of radial profile in the outer disk R > Rpeak, as shown in Figure 10, means that there is no cut-off of the stellar component at R = 14–15 kpc as stated by Ruphy et al. (1996), which is also discovered by B16.


The choice of SFH to infer evolutionary parameters intrinsically carries a degeneracy with the adopted functional form. A more direct approach is to quantify the light-weighted contribution of recent star formation by measuring the relative contribution to the integrated stellar spectrum of short-lived massive stars with respect to long-lived lower-mass stars. Balmer absorption lines reach their maximum strength in A-type stars with a spectral break at 3646 Å. Stars of lower mass and lower effective temperature produce metal absorption lines (CaII H & K, Fe, and Mg) which result in a sharp spectral break at 4000 Å. Moreover, the underlying continuum changes shape with time, progressively losing emission in the NUV-blue spectral range while flattening in the NIR. The different evolutionary rates of the stars producing the lines and their fractional contribution to the optical light at fixed mass enable tracing the evolutionary stage of a galaxy. In Fig. 11 (upper panels) we show the evolution of the spectral break measured through the DB definition (Kriek et al. 2006) and the Dn4000 definition (Balogh et al. 1999), as well as the relative strength (the ratio) between the two (lower left panel). Lighter shaded curves show the variation with increasing duration of star formation. The ratio is shown as a function of age of composite templates built with a short truncated SFH. The ratio is only mildly dependent on dust reddening because the two indices share a similar wavelength range. Moreover, the two indices are fairly robust against low resolution. The ratio varies strongly during the first 1 Gyr or so, reaching its maximum around 0.3–0.5 Gyr. Eventually, it drops below 1 when the light-weighted contribution from A-type stars fades away. Constant star formation instead results in a ratio of ∼1.1 that is rather constant with time. Varying the metallicity of the input templates has the effect of anticipating the transition to DB/Dn4000 > 1. This effect is strongest when supersolar metallicity (2.5Z⊙) is adopted in which case the transition is reached at 0.9 Gyr. We suggest that this ratio could be used to spot PSB galaxies with high dust attenuation along and across the UVJ diagram when high-resolution spectra are unavailable.


H2O ice on Pluto has long escaped spectroscopic detection, and based on initial New Horizons data appears to be exposed only in a number of specific locations, usually associated with red color, suggestive of water ice/tholin mix (Grundy et al. 2015; Cook et al. 2015). Nonetheless, water ice is likely to be ubiquitous in Pluto’s near subsurface, given its cosmogonical abundance, Pluto’s density, and its presence on Charon’s surface6. Absorption coefficients for pure water ice (kH2O) at sub-mm-to-cm wavelengths are discussed extensively by Mätzler (1998), who also provides several analytic formulations to estimate them as a function of frequency and temperature along with illustrative plots. We use the Mishima et al. (1983) formulation (see Appendix of Mätzler 1998). Its applicability is normally restricted to temperatures above 100 K, but Fig. 2 of Mätzler (1998) indicates the trend with temperature. Absorption coefficients extrapolated to 50 K (estimated as half the values at 100 K) are shown in Fig. 5. At 500 μm, our best estimate is kH2O = 0.25 cm-1, comparable to the above values for CH4 and N2 ices. The corresponding penetration length is therefore comparable to the diurnal skin depth but remains negligible compared to the seasonal skin depth, even for seasonal Γ = 25 MKS. According to these calculations, the seasonal layer would be probed only at a wavelength of ~4 mm and beyond. We also remark that the expression from Mishima et al. (1983) would give a penetration depth of 56 m at 2.2 cm, which is an order of magnitude larger than indicated by the laboratory measurements of Paillou et al. (2008). In addition, small concentrations of impurities can dramatically reduce the microwave transparency of water ice (e.g., Chyba et al. 1998 and references therein). Therefore, the above calculations likely indicate upper limits to the actual penetration depth of radiation in a H2O ice layer, from which we conclude that the seasonal layer is not reached at the Herschel wavelengths.


The role of cluster mergers in producing shocks underlying radio relics at cluster peripheries is well supported by observational evidence (Giovannini, Tordi & Feretti 1999; Bagchi et al. 2006; van Weeren et al. 2009; Venturi et al. 2013). A different class of relics, proposed to be fading radio galaxy lobes or such lobes revived due to adiabatic compression (Enßlin & Gopal-Krishna 2001), are also well known to occur in clusters. Examples of relics in this category are those in Abell 4038 (Slee & Roy 1998; Kale & Dwarakanath 2012) and Abell 85 (Slee et al. 2001). In A168 we find an arc-like relic at the periphery (B) and a smaller steep-spectrum relic (A) in its wake, as seen projected in the plane of the sky. The intracluster medium (ICM) in the cluster is elongated along the north–south direction and the orientation of B is perpendicular to this direction. Merger-shock-related relics have been found to be oriented preferentially perpendicular to the elongation axis of the ICM (van Weeren et al. 2011). The flat spectral index of B, arc-like morphology and orientation thus support the scenario that a cluster merger along the north–south direction led to an outgoing merger shock that accelerated electrons, which are detected as relic B. Relic A has a steep and curved spectrum (Fig. 3) indicative of an ageing population of relativistic electrons. We propose that relic A is a candidate adiabatically compressed lobe of a radio galaxy, the compression having been caused by the outgoing shock at B. Simulations of adiabatically compressed cocoons of radio galaxies have shown that, as compression proceeds, the cocoon is torn into filamentary structures, which can appear like a single torus or multiple tori at late stages (Enßlin & Brüggen 2002). The morphology of relic A is complex and filamentary, which compares well with the predicted structures of compressed cocoons. The morphology of B is not smooth at the outer boundary, but shows kink-like features connected to A, implying a possible distortion due to the presence of a radio cocoon in the path of the outgoing shock that led to the formation of A.


If PTFO 8-8695 is accreting material onto the star from a gas disk devoid of small grains, the excess Hα emission may result entirely from the accreting material whether or not there is a planetary companion present. This accretion-related emission would presumably be similar to Hα emission seen in other CTTSs, many of which also have close companions. If there is a low-mass companion to PTFO 8-8695, accretion from a disk may be through accretion streams such as those proposed by Artymowicz & Lubow (1996) (see also Günther & Kley 2002). At this time, it is not known if a planetary mass companion can excite accretion streams such as those modeled by Artymowicz & Lubow (1996) and Günther & Kley (2002). A few CTTSs binaries are thought to potentially be accreting through accretion streams. These include DQ Tau (Basri et al. 1997; Mathieu et al. 1997), UZ Tau E (Jensen et al. 2007), AK Sco (Alencar et al. 2003), KH 15D (Hamilton et al. 2012), and the eclipsing binary system CoRoT 223992193 in NGC 2264 (Gillen et al. 2014). None of these stars shows the type of Hα variations seen in PTFO 8-8695 where the accretion-related emission appears to move from one side of the line profile to the other as it spirals onto one or both of the stars. This type of line profile behavior is also not seen in the Hα profile variations of single CTTSs in extensive studies of their line profile variability (e.g., Giampapa et al. 1993; Johns & Basri 1995a, 1995b; Johns-Krull & Basri 1997; Oliveira et al. 1998; Alencar et al. 2001), nor is it predicted from theoretical models of magnetospheric accretion such as those shown in Kurosawa & Romanova (2013). While we cannot completely rule out accretion from a tenuous disk as the source of the excess Hα emission observed in PTFO 8-8695, we argue that this is not the most likely explanation of the observed emission. Deep mid-IR or millimeter continuum observations, or a deep search for close circumstellar disk gas emission (e.g., H2 emission, see France et al. 2012), could shed light on whether there is a tenuous disk around this star feeding accretion onto it.


Using AIA intensity images in six EUV bandpasses, a differential emission measure (DEM) analysis (Cheng et al. 2012; Shen et al. 2015) is performed for the solar flare at 17:03:37 UT, when the oscillations are pronounced. Figure 8 panels b and c plot the DEM profiles in the flaring loop and the background corona, respectively. They contain the same region with an FOV of 3″ × 3″, as enclosed by the red boxes in Fig. 1e. The black profile in each panel is the best-fit DEM solution to the observed fluxes. The colored rectangles represent the errors of the DEM curve, which are calculated from 100 Monte Carlo (MC) realizations of the observational data (Cheng et al. 2012; Tian et al. 2016; Li et al. 2017b). The average temperature (T) and emission measure (EM) inside and outside (background corona) of the flaring loop are also estimated according to their errors, respectively. For example, the confident temperature (log T) range inside the flaring loop is 6.0−7.5, while that outside of the flaring loop is 5.8−7.1, since the temperatures in solar flare are much higher than that in the background corona. Therefore, the number density inside the flaring loop can be estimated with

n
e

=

E
M
/
w

$ n_e\,{=}\,\sqrt{EM/w} $

 by assuming a filling factor of 1.0 (Tian et al. 2016; Li et al. 2017b), and we can obtain a lower limited density inside the flaring loop of ∼4.7 × 1010 cm−3. On the other hand, the effective LOS depth (

l

≈

H
π
r

∼

4

×

10
10

$ l\,{\approx}\sqrt{H\pi r}\,{\sim}\,4\,{\times}\, 10^{10} $

 cm), instead of the loop width, is applied to calculate the number density outside of the flaring loop (Zhang & Ji 2014; Zucca et al. 2014; Su et al. 2016; Li et al. 2017b), and we get 9.1 × 108 cm−3. Finally, a number density ratio (rd = n0/ne) of ∼0.02 between outside and inside of the flaring loop is determined, which is very close to the density contrast from recent observations (Tian et al. 2016; Li et al. 2017b).


Many mechanisms that probably lead to quenching have been proposed, and surely we need some parameters to discriminate between these mechanisms. One of such critical parameters we are looking for is the quenching timescale, which varies from one mechanism to another. Most of these timescales are obtained from simulations (Wright et al. 2019; Wetzel et al. 2013; Walters et al. 2022). Among mass-dependent quenching mechanisms, the supernova feedback is a strong physical process that quickly quenches star formation in ∼0.1 Gyr (Ceverino & Klypin 2009). On the other hand, the AGN feedback timescale is still under debate. Quasar mode AGN feedback is a strong process. Quasars’ lifetimes are not long ( 0.1 Gyr, Hopkins et al. 2006), and the gas outflow driven by quasars can quench star formation at million-year-level timescales (Smethurst et al. 2021). On the other hand, the radio mode of AGN feedback is a slow process (Best et al. 2005); it takes up to and beyond 1 Gyr to establish a balance between cooling and heating to reach a low star formation rate phase (Fabian 2012). In Schawinski et al. (2014) the authors maintain that the timescale of AGN feedback leading to a quenching can be related to the host galaxy type. Furthermore, Hirschmann et al. (2017) simulated the star formation history (SFH) of galaxies with AGN and found that the quenching timescale can have a wide range, from a few hundred Myr to a few Gyr. For environmental quenching mechanisms, strangulation is a long-term process that lasts a few billion years (Peng et al. 2015). Merger-driven quenching does not happen directly after galaxy merger events; a median delay time of 1.5 Gyr is expected, and the timescale varies over a wide range (Rodríguez Montero et al. 2019). Ram pressure stripping is a rapid type of quenching, with a timescale of ∼0.2 Gyr (Steinhauser et al. 2016). Thus, we can use the quenching timescale to determine some of the physical mechanisms involved.


The field of extrasolar planetary transits is one of the most productive and innovative subject in astrophysics in the last decade. Transit observations can be used to measure the size of planets, their orbital parameters (Seager and Mallén-Ornelas 2003), and stellar properties (Mandel & Agol 2002; Howarth 2011), to study the atmospheres of planets (Brown 2001; Charbonneau et al. 2002; Tinetti et al. 2007), and to detect small planets (Miralda-Escudé 2002; Agol et al. 2005) and exomoons (Kipping 2009a, 2009b). In particular, the study of planetary atmospheres requires a high level of photometric precision, i.e., one part in ∼104 in stellar flux (Brown 2001), which is comparable to the effects of current instrumental systematics and stellar activity (Berta et al. 2011; Ballerini et al. 2012), hence the necessity of testable methods for data detrending. In some cases, different assumptions, e.g., using different instrumental information or functional forms to describe them, leed to controversial results even from the same data sets; examples in the literature are Tinetti et al. (2007), Ehrenreich et al. (2007), Beaulieu et al. (2008) and Désert et al. (2009, 2011) for the hot-Jupiter HD 189733b, and Stevenson et al. (2010), Beaulieu et al. (2011) and Knutson et al. (2011, 2014) for the warm-Neptune GJ436b. Some of these controversies are based on Spitzer/IRAC data sets at 3.6 and 4.5 μm. The main systematic effect for these two channels is an almost regular undulation with period ∼3000 s, so called pixel-phase effect, as it is correlated with the relative position of the source centroid with respect to a pixel center (Fazio et al. 2004; Morales-Caldéron et al. 2006). Conventional parametric techniques correct for this effect by dividing the measured flux by a polynomial function of the coordinates of the photometric centroid; some variants may include time-dependence (e.g., Stevenson et al. 2010; Beaulieu et al. 2011). Newer techniques attempt to map the intra-pixel variability at a fine-scale level, e.g., adopting spatial weighting functions (Ballard et al. 2010; Cowan et al. 2012; Lewis et al. 2013) or interpolating grids (Stevenson et al. 2012a, 2012b). The results obtained with these methods appear to be strongly dependent on a few assumptions, e.g., the degree of the polynomial adopted, the photometric technique, the centroid determination, calibrating instrument systematics over the out-of-transit only or the whole observation (e.g., Beaulieu et al. 2011; Diamond-Lowe et al. 2014; Zellem et al. 2014). Also, the very same method, applied to different observations of the same system, often leads to significantly different results. Non-parametric methods have been proposed to guarantee a higher degree of objectivity (Carter & Winn 2009; Thatte et al. 2010; Gibson et al. 2012; Waldmann 2012, 2014; Waldmann et al. 2013). Morello et al. (2014, 2015) reanalyzed the 3.6 and 4.5 μm Spitzer/IRAC primary transits of HD 189733b and GJ436b obtained during the cryogenic regime, so called “cold Spitzer” era, adopting a blind source separation technique, based on an Independent Component Analysis (ICA) of individual pixel timeseries, in this paper called “pixel-ICA”. The results obtained with this method are repeatable over different epochs, and a photometric precision of one part in ∼104 in stellar flux is achieved, with no signs of significant stellar variability as suggested in the previous literature (Désert et al. 2011; Knutson et al. 2011). The use of ICA to decorrelate the transit signals from astrophysical and instrumental noise, in spectrophotometric observations, has been proposed by Waldmann (2012, 2014) and Waldmann et al. (2013). The reason to prefer such blind detrending methods is twofold: they require very little, if any, prior knowledge of the instrument systematics and astrophysical signals, therefore they also ensure a higher degree of objectivity compared to methods based on approximate instrument systematics models. As an added value, they give stable results over several data sets, also in those cases where more conventional methods have been unsuccessful. Recently, Deming et al. (2015) proposed a different pixel-level decorrelation method (PLD) that uses pixel timeseries to correct for the pixel-phase effect, while simultaneously modeling the astrophysical signals and possible detector sensitivity variability in a parametric way. PLD has been applied to some Spitzer/IRAC eclipses and synthetic Spitzer data, showing better performances compared to previously published detrending methods.


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.