For this comparison we rely on HST broad-band magnitudes, Lyα line fluxes, Lyα EW0 estimates, spectral UV slopes β, continuum magnitudes, and Lyα FWHM from the catalog that will be presented by Kerutt et al. (2021). This LAE study is based on the same data and source identification (see Sect. 3) as the current study, but focuses on properties of the emanating Lyα emission. The Lyα emission fluxes correspond to the measured flux within 3D apertures of three Kron (1980) radii as measured by LSDCat when detecting sources in the MUSE data cubes. We use these Lyα fluxes as opposed to obtaining them directly from the TDOSE spectra, as the TDOSE extractions are based on the assumption that the morphological extent of the line emission follows the continuum morphology of the modeled HST images (Sect. 4 and Schmidt et al. 2019). However, Lyα emission is known to be extended beyond the continuum (Steidel et al. 2011; Momose et al. 2014; Wisotzki et al. 2016, 2018; Leclercq et al. 2017, 2020) and fluxes based on the TDOSE spectra would therefore be biased. The LSDCat Kron radii fluxes therefore better represents the actual Lyα flux emitted by the LAEs. As for the secondary UV emission lines, the EW0(Lyα) values were calculated by comparing the fluxes to the continuum flux densities estimated from a continuum represented by a power law fλ ∝ λβ. To obtain the spectral slope, Kerutt et al. (2021) first determined the magnitudes from available ancillary broad-band HST photometry, by fitting the rest-frame UV morphology for each of the LAEs using GALFIT (Peng et al. 2010, 2002). This provided morphological parameters for all LAEs including a measure of their effective radius. The estimated absolute UV magnitude at 1500 Å is also based on these GALFIT models. The spectral slope β was then obtained from fitting the continuum power law to the GALFIT-based HST magnitudes. To avoid large scatter in the EW0 measurements presented and analyzed by Kerutt et al. (2021), the EWs are based on the median β for the full LAE sample of β = −1.97 similar to what was done for the secondary UV emission lines presented here. The FWHM of the Lyα emission was measured for each source by fitting a skewed Gaussian profile (Eq. (2) by Shibuya et al. 2014) to the Lyα line profiles in 1D spectral extractions weighted by the MUSE PSF to maximize S/N. These fits also provide a Lyα redshift which is more precise than the lead line redshits provided by the LSDCat source identification. We therefore use these redshifts for the analysis of the Lyα velocity offsets described in Sect. 8. Finally, Kerutt et al. (2021) provide estimates of the systemic redshifts based on the FWHM and peak separation between any double-peaked LAEs (identified through visual inspection of the 1D spectra) in the sample based on the empirical relations presented by Verhamme et al. (2018). We note that a handful of the z > 2.9 objects studied here are not included in the Kerutt et al. (2021) catalog, as their selection was based only on non-AGN objects with leading Lyα emission based on the LSDCat selections (Sect. 3). Hence, for z > 2.9, the objects with IDs 121033078, 601381485, 720470421, 722551008, 722731033, and 723311101 are not included in the LAE parameter comparisons in the following. For further details and for the full value-added catalog of Lyα-related quantities we refer to Kerutt et al. (2021).


Radio astronomy is recognized as one of the most effective techniques to search for interstellar molecules. By comparing the spectra of candidate molecules in the laboratory with the spectra observed in astronomical surveys, we can determine whether these molecules exist in interstellar space. Therefore, it is necessary to provide rotational spectra of candidates for astronomical detection. Radio astronomy has helped to detect several sulfur-containing molecules in the ISM in recent years: in particular, thiols, the sulfur analogs of alcohols. Methanethiol (or methyl mercaptan, CH3SH) was detected in the Sagittarius B2 (Sgr B2) region of the center of our Galaxy (Linke et al. 1979; Gibb et al. 2000; Müller et al. 2016; Rodríguez-Almeida et al. 2021) and in the protostar IRAS 16293-2422 (Majumdar et al. 2016). Two groups reported to have detected several signs of ethanethiol (C2H5SH) in Sgr B2 (Müller et al. 2016) and Orion (Kolesniková et al. 2014). Most recently, Rodriguez-Almeida reported the first unambiguous detection of ethanethiol in the ISM, toward the G+0.603-0.027 molecular cloud (Rodríguez-Almeida et al. 2021). Moreover, several sulfur-containing species have been observed in comets (Altwegg et al. 2017). Some recent efforts, both from spectroscopy and astronomical searches, to detect S-stitutes of other classes of compounds have also been reported. For instance, thioformic acid (HC(O)SH) was very recently detected in G+0.693–0.027. Its trans-isomer has an abundance of ~1 × 10–10 (Rodríguez-Almeida et al. 2021). Conversely, thioformamide (NH2CHS), the counterpart of for-mamide (NH2CHO), was characterized in the laboratory up to 660 GHz, and its transitions were searched for toward the hot cores Sgr B2(N1S) and Sgr B2(N2), but it was not detected (Motiyenko et al. 2020). The rotational spectrum of thioac-etamide was recently analyzed in the 59.6–110.0 GHz frequency region (5.03–2.72 mm). Its emission was searched for in regions associated with star formation using the IRAM 30 m ASAI observations toward the prestellar core L1544 and the outflow shock L1157–B1. The molecule was not detected, but the study allowed placing constraints on the thioacetamide abundances (Maris et al. 2019; Remijan et al. 2022).


Solar vortex tubes can be spontaneously generated by turbulent convection. In simulations of quiet Sun regions, vortices are found along intergranular lanes (Shelyag et al. 2011a; Kitiashvili et al. 2012; Moll et al. 2012; Silva et al. 2020). These structures have an average lifetime of around 80 s (Silva et al. 2021) and a radius between 40 and 80 km (Shelyag et al. 2013; Silva et al. 2020). Solar kinetic vortex tubes (Silva et al. 2021) act as a sink for magnetic field, creating magnetic flux tubes that expand with height (Kitiashvili et al. 2012; Moll et al. 2012; Silva et al. 2020). The concentration of magnetic flux leads to a high magnetic field tension, which can prevent the magnetic field lines from being twisted by the rotational motion (Shelyag et al. 2011b; Moll et al. 2012; Nelson et al. 2013; Silva et al. 2021). In some cases, twisted magnetic flux tubes appear close enough to flow vortices, leading to magnetic and kinetic vortex structures closely coexisting in regions with high plasma-β (Wedemeyer & Steiner 2014; Rappazzo et al. 2019; Silva et al. 2021). The vortical motions can still trigger perturbations along magnetic lines that could lead to wave excitation, e.g., Battaglia et al. (2021). The vorticity evolution in the magnetized solar atmosphere is mainly ruled by the magnetic field, which also influences the general shape of vortices (Shelyag et al. 2011a). Based on the analysis of swirling strength, the part of the vorticity only linked to swirling motion (Shelyag et al. 2011b; Canivete Cuissa & Steiner 2020) showed that the magnetic terms in the swirling equation evolution tend to cancel the hydrodynamic terms close to the solar surface, whereas the magnetic terms dominate alone the production of swirling motion in the chromosphere. The magnetic field also tends to play an important role in the plasma dynamics along the whole vortex tube, as the Lorentz force has a magnitude comparable to the pressure gradient (Silva et al. 2020; Kitiashvili et al. 2013). High-speed flow jets have also been linked to simulated vortex tubes, driven by high-pressure gradients close to the photosphere and by Lorentz force in the weakly magnetized upper solar photosphere (Kitiashvili et al. 2013). In general, the averaged radial profile of magnetic field, angular velocity, pressure gradient inside of the vortex tube at the lower chromosphere and photosphere levels show similar behavior (Silva et al. 2020).


The leading edges of the transients normally leave bright traces in the images of visible light, inspiring many methods that were developed to derive their locations and velocities, such as the icecream cone model (Fisher & Munro 1984), the graduated cylindrical shell (GCS) model (Thernisien 2011), geometric triangulation methods (Liu et al. 2010), mask-fitting methods (Feng et al. 2012), and trace-fitting methods including the point-p, fixed-Φ, harmonic mean, and self-similar expansion fitting methods (e.g., Sheeley et al. 1999; Howard et al. 2006; Davies et al. 2012; Möstl & Davies 2013). To derive the velocity distribution inside one transient rather than only at its leading edge, some other techniques have been proposed. Colaninno & Vourlidas (2006) applied an optical flow tool to extract the velocity vector of a coronal mass ejection (CME) in digital images. Feng et al. (2015) derived the radial velocity profiles of the whole CME from the spatial distribution of its density given by the mass continuum equation. A cross-correlation method was applied to derive continuous 2D speed maps of a CME from coronagraphic images by Bemporad et al. (2018). In their work, the radial shift pixel by pixel is determined by maximizing the cross correlation between the signal in a radial window at one frame and the signal in a radial shifted window at the previous frame, and the radial speed just equals the radial shift over the time interval between the two frames. Ying et al. (2019) improved this cross-correlation method by analyzing data in three steps: forward step (FS), backward step (BS), and average step (AS). In the FS (BS), the 2D velocity map between the current and the previous (next) frame is constructed with almost the same method as Bemporad et al. (2018). In the AS the average, velocity is obtained from the FS and BS. The velocities derived by all these methods are the component of the flow velocity vector projected onto the POS. This may underestimate the velocity especially for transients that do not propagate in the POS. Methods such as the polarizaition ratio technique (Moran & Davila 2004; DeForest et al. 2017) or the local correlation tracking (LCT) method (Mierla et al. 2009) can derive the 3D geometric information of the whole transients, but not the velocity distribution. Bemporad et al. (2018) chose the propagating direction averaged over the whole CME derived by the polarization ratio technique to correct the radial speed in the 2D maps, but the key information along the LOS is still lacking.


Atmospheric superrotation is characterized by eastward wind at the equator, which means the atmosphere there has a higher angular momentum than the solid surface. Atmospheric superrotation is a common phenomenon across the universe. In the solar system, superrotation exists in the atmospheres of Venus, Titan, Saturn, and Jupiter, as well as the stratospheric atmosphere of Earth during the westerly phase of the quasi-biennial oscillation (e.g., Kraucunas & Hartmann 2005; Schneider & Liu 2009; Lutsko 2018; Read & Lebonnois 2018). In order to maintain atmospheric superrotation, there must be momentum transports from higher latitudes to the equator against friction or other processes, according to angular momentum conservation (Hide 1969; Held 1999; Showman et al. 2013). This up-gradient transport into the jet can result from Rossby waves, coupled Rossby–Kelvin waves, mixed Rossby–gravity waves, wave–jet resonance, barotropic instability, or baroclinic instability (Suarez & Duffy 1992; Del Genio & Zhou 1996; Joshi et al. 1997; Lee 1999; Williams 2003; Kraucunas & Hartmann 2005; Schneider & Liu 2009; Caballero & Huber 2010; Mitchell & Vallis 2010; Showman & Polvani 2010, 2011; Showman et al. 2010; Liu & Schneider 2011; Arnold et al. 2012; Pinto & Mitchell 2014; Tsai et al. 2014; Wang & Mitchell 2014; Laraia & Schneider 2015; Lutsko 2018; Read & Lebonnois 2018; Pierrehumbert & Hammond 2019). For example, Kraucunas & Hartmann (2005) suggested that in an Earth-like atmosphere, equatorial superrotation can be generated by equatorward stationary eddy momentum convergence, which is associated with zonal variations in the diabatic heating at low latitudes. Mitchell & Vallis (2010) studied the transition from current Earth-like atmospheric circulation to an equatorial superrotation state. They found that during the spin-up period, superrotation is generated by equatorward momentum convergence associated with both barotropic and baroclinic instabilities.


This scenario could also explain the reason why star-forming galaxies with a relatively thin disk appeared around z ∼ 1, because the hot-mode accretion is expected not to dominate even in massive halos at z ≳ 2 owing to gas supply through the cold gas stream (e.g., Dekel et al. 2009a; Kereš et al. 2009). Some of the gas accreting onto a dark matter halo is expected to penetrate surrounding hot gas in a form of filaments of dense and cold infalling gas and directly accrete onto the central galaxy at such high redshift, where the mass accretion rate and matter density tend to be high. Such direct gas supply through the filaments of cold gas could make the gas disk of the central galaxy more turbulent and gravitationally unstable, which leads to thick and clumpy stellar disk and bulge formation/growth in some cases (e.g., Dekel et al. 2009b; Ceverino et al. 2010; Dekel & Burkert 2014). Thus, it seems to be difficult to form the thin stellar disks even in massive halos at z ≳ 2. After the hot-mode accretion starts to dominate at z ∼ 2 in relatively massive halos, the thin stellar disks may be gradually formed from thinner gas disks and appear around z ∼ 1. If some of these star-forming galaxies with a thin stellar disk quench and evolve into passively evolving galaxies without a violent morphological change as discussed in the previous section, it is understood that the fraction of passively evolving galaxies with a thin shape increases with time at z  1 rather than higher redshifts. Since more massive star-forming galaxies have a sufficient time to form a thin disk through the hot-mode accretion in earlier epochs in this scenario (Noguchi 2019), passively evolving galaxies with a thin shape may also be provided preferentially in more massive galaxy populations at z ∼ 1. This could explain our result that passively evolving galaxies with Mstar = 1010.5–1011 M⊙ already show a thinner shape than those with 1010–1010.5 M⊙ at 0.6  z  1.0. In fact, Bezanson et al. (2018) reported that ∼64% of quiescent galaxies with Mstar ∼ 1010.5–1011 M⊙ at 0.6  z  1.0 show a significant rotation, while those massive galaxies with Mstar > 1011 M⊙ show no or little rotation in the LEGA-C survey. Such quiescent galaxies with a significant rotation might be recently quenched galaxies with a relatively thin disk that has grown through the hot-mode gas accretion since z ≲ 2.


NGC 2992 has been observed at different flux levels (e.g. Fig. 14) and in Marinucci et al. (2018) a detailed analysis of XMM–Newton exposures of this object is presented. In order to provide an holistic view of the spectral properties of the central engine in NGC 2992, we tested our 2019 best-fitting model (model 1 presented in Section 4.3) on the exposures where the source was found in its lowest and highest states. In particular, we considered the XMM–Newton archival observations taken on 2003-05-19 and 2010-11-28 (Obs.IDs. 01479203014 and 0654910901, respectively). We thus reproduced them directly adopting the best-fitting model found for orbit 2 and show it in Fig. 12. We accounted for the different flux states computing the normalization of the primary emission, its associated reflected component, and the one of the apec and Cloudy tables. All the other parameters have been kept fixed to their corresponding best-fitting values already quoted in Table 5. Moreover, to account for the broad emission line found required by the 2003 data, we added a Gaussian component whose width was kept fixed to σ = 400 eV, in accordance with what literature papers (e.g. Nandra et al. 1997; Shu et al. 2010). This basic procedure led us to the fits shown in Fig. 15, with statistics of χ2/d.o.f. = 218/170 and χ2/d.o.f. = 181/140. In the high flux level, (F2–10 keV = (9.5 ± 0.1) × 10−11 erg cm−2 s−1), the normalization of the power law is Normpo = (3.00 ± 0.01) × 10−2 ph. keV−1 cm2 s−1, about twice of what found in 2019 while the amount of reflected flux is fully consistent with what found in 2019 as we obtained NormMyTorus = (9.7 ± 3.7) × 10−2 ph. keV−1 cm2 s−1. On the other hand, in the 2010 low flux level exposure (F2–10 keV  = (2.9 ± 0.2) × 10−12 erg cm−2 s−1), we found the power-law normalization to be Normpo = (4.7 ± 0.2) × 10−4 ph. keV−1 cm2 s−1 about 20–30 times lower than in 2019. The normalization of the reflected component modelled using MyTorus is NormMyTorus = (9.5 ± 1.2) × 10−3 ph. keV−1 $\rm cm^{2}$ s−1, a factor of ∼10 less than in 2019. Therefore, on very long time-scales and during a prolonged low state of the source in 2010, the strength of the reflector appears to respond to the continuum. However, the smaller value of the reflected component found in 2010 can be explained by the torus reflecting the primary continuum of NGC 2992 during a low flux state. Observing the light curves in Fig. 1, before 2010 NGC 2992 was observed in a very low flux state, even lower than the one in 2021. Such a long-term adjustment suggests that reflected spectrum emerges far from the central engine. Finally, in accordance with previous studies, the Fe Kα emission line of NGC 2992 has an unresolved component correlating with the primary flux and emerging from the broad line region. However, the current MyTorus-based model accounts for whole Fe Kα flux (see residuals in Fig. 15) and data do not require any additional Gaussian component. Below 1 keV, the non-variable behaviour of the distant scattering off the NLR can be witnessed in the top panels of Fig. 7 or in the first bin of the excess variance spectra in both Figs 6 and  8.


Optical-NIR Spectral Energy Distribution (SED) modelling of diffuse light (e.g. Carson et al. 2015; Dale et al. 2016) at high spatial resolution can provide a map of the spatial variation and composition of the main stellar population components of the NSC, as well as their host galaxy. Such analysis can also unveil (an additional) contribution to the optical-NIR light from accretion on to an obscured nuclear MBH and/or nuclear star formation activity (e.g. Noll et al. 2009; Drouart et al. 2016). To understand NSC formation, it is therefore critical to be able to disentangle such degeneracies. For example, follow-up spectroscopic observations aiming at decomposing the main stellar populations and the dynamical imprint of a MBH rely on a good mass model to predict the stellar population velocity profile (e.g. Häring-Neumayer et al. 2006; Seth et al. 2010; Neumayer et al. 2011; Neumayer & Walcher 2012; Nguyen et al. 2018). Observations of NSCs have shown that although they contain young stellar populations and extended star formation histories (SFHs), the most dominant one by mass is the oldest (≳3 Gyr), where more than 50 per cent of the mass of the cluster has formed (e.g. Walcher et al. 2006; Kacharov et al. 2018, from spectral modelling in the optical). Therefore, characterizing the spatial structure of NSCs in the NIR is of particular importance, because that is where most of the stellar light of the old stellar population is emitted that allows us to trace most of the gravitating mass. Therefore, characterizing NSCs in the NIR can provide additional constraints as to which of the leading NSC formation scenarios had a major role in the formation of particular NSC. However, to be able to achieve this for a larger sample and of more distant galaxies, efficient high spatial resolution observations are required, such as with the presented here wide-field ground-layer AO NIR observations in the NIR with ARGOS at the LBT.


The initially used automated methods for detection of solar activity in the solar images are based on the predefined rules inferred from the appearance and usual characteristics of the structures in the solar atmosphere (Henney & Harvey 2005; Krista & Gallagher 2009; Pérez-Suárez et al. 2011). However, it is not possible to capture all the unique solar structures using generally defined rules. Therefore, methods with more advanced mathematical algorithms were introduced. The Spatial Possibilistic Clustering Algorithm (SPoCA) (Barra et al. 2009; Verbeeck et al. 2014) or Coronal Hole Identification via Multi-thermal Emission Recognition Algorithm (CHIMERA) (Garton, Gallagher & Murray 2018) have been found to be very effective and are widely used in online solar data visualization tools1$^,$ .2 The SPoCA also provides entries for catalogues of coronal holes and active regions within the Heliophysics Events Knowledgebase (HEK) (Hurlburt et al. 2012) that is commonly used in the SolarSoft (Freeland & Handy 1998) and SunPy (The SunPy Community et al. 2020) frameworks. As will be presented later, these algorithms still have limitations for the precise segmentation of structures in the solar corona. Due to the advances in computer vision in recent years, approaches based on machine-learning techniques are able to extend the standard methods (Aschwanden 2010). Conventional machine-learning techniques as support vector machine (SVM), Decision Tree, or Random Forest could improve the detection of coronal holes as they provide automated distinguishing from filaments in EUV solar images (Reiss et al. 2015; Delouille et al. 2018). On the assumption that the techniques based on convolutional neural networks (CNN) are the most convenient techniques for image segmentation tasks (Lecun, Bengio & Hinton 2015), a new era in automated processing of solar images has begun. Illarionov & Tlatov (2018) demonstrated that for the segmentation of coronal holes, CNN provides quantitatively comparable results as the SPoCA and CHIMERA algorithms. Jarolim et al. (2021) provided a method called CHRONOS based on CNN with progressively growing network approach for robust segmentation of coronal holes.


More recently, Lyman α haloes (LAHs) have been discovered around star-forming galaxies that show Ly α emission far beyond the galaxies’ optical bodies, tracing the circumgalactic rather than interstellar gas (e.g. Hayes et al. 2013). While LAHs are fainter and smaller than LABs in their Ly α extent, they might be a generic feature around Ly α emitting galaxies. Narrow-band imaging can efficiently detect LAHs at targeted redshifts through stacking (Hayashino et al. 2004; Steidel et al. 2011; Matsuda et al. 2012; Feldmeier et al. 2013), and narrow-band surveys enable ultradeep, blind samples of LAHs around distant galaxies (Momose et al. 2014, 2016; Kakuma et al. 2019). In the last years modern surveys performed with integral field unit (IFU) spectrographs on 10 m-class telescopes, such as the Multi Unit Spectroscopic Explorer (MUSE) and the Keck Cosmic Web Imager (KCWI), take place. These new instruments allow the study of individual, faint LAHs opposed to previous narrow-band stacks. Along with the IFUs’ spectral resolution these recent surveys largely increase the information available from LAH observations. Hundreds of individually extended Lyman α haloes at z ≳ 2 have been revealed since (Wisotzki et al. 2016). Many of these are specifically targeted samples which focus on bright quasars (Borisova et al. 2016; Cai et al. 2019; Guo et al. 2020; O’Sullivan et al. 2020), based on strong earlier evidence of enhanced Ly α emission around active galactic nuclei (AGNs Cantalupo et al. 2014; Arrigoni Battaia et al. 2016; Arrigoni Battaia et al. 2019; Farina et al. 2019). Others exploit the large field of view of MUSE, in particular, to conduct blind surveys for LAHs around more typical, generally star-forming galaxies (Leclercq et al. 2017; Wisotzki et al. 2018; Leclercq et al. 2020). At the same time, follow-up with other instruments such as ALMA reveals complementary views on other gas phases within LAHs including CO (Emonts et al. 2019).


The astrophysical environments of r-process events in the cosmos usually involve explosive physical conditions, because very high neutron densities are required. The neutron-rich isotopes produced along the r-process path have a very short half-life, causing the neutron-rich nuclei to β-decay over time-scales of the order of milliseconds, if they did not undergo further rapid neutron capture. Examples of r-process sites that have been proposed and investigated by theoretical studies include (i) neutrino-driven winds from proto-neutron stars (NSs; Takahashi, Witti & Janka 1994; Woosley et al. 1994; Qian & Woosley 1996; Hoffman, Woosley & Qian 1997; Otsuki et al. 2000; Thompson, Burrows & Meyer 2001; Wanajo et al. 2009; Hansen et al. 2013; Wanajo 2013); (ii) neutrino-driven winds from highly magnetic and potentially rapidly rotating proto-magnetars (Thompson 2003; Thompson, Chang & Quataert 2004; Metzger, Thompson & Quataert 2007, 2008; Vlasov, Metzger & Thompson 2014; Vlasov et al. 2017; Thompson & ud-Doula 2018); (iii) neutrino-driven winds around the accretion disc of a black hole (Pruet, Thompson & Hoffman 2004; Metzger et al. 2008; Wanajo & Janka 2012; Siegel, Barnes & Metzger 2019); (iv) electron-capture SNe (see e.g. Wanajo et al. 2009; Cescutti et al. 2013; Kobayashi, Karakas & Lugaro 2020); (v) magneto-rotationally driven SNe (Burrows et al. 2007; Winteler et al. 2012; Cescutti & Chiappini 2014; Mösta et al. 2014, 2015, 2018; Nishimura, Takiwaki & Thielemann 2015; Nishimura et al. 2017; Halevi & Mösta 2018; Reichert et al. 2021); and (vi) neutron-star mergers (Lattimer et al. 1977; Freiburghaus, Rosswog & Thielemann 1999; Argast et al. 2004; Goriely, Bauswein & Janka 2011; Rosswog 2013; Matteucci et al. 2014; Cescutti et al. 2015; Vincenzo et al. 2015; Kobayashi et al. 2020). Since it is likely that all these mechanisms have contributed to r-process nucleosynthesis at some level, the theoretical studies have focused on exploring the frequency of each event and the predicted template of the corresponding r-process ejecta.


Previous Herschel studies confirmed numerical calculations (e.g., Nagai et al. 1998) showing that a parallel orientation between filaments and the local magnetic field is to be expected for filaments having low column densities (e.g., Peretto et al. 2012; Busquet et al. 2013; Palmeirim et al. 2013; André et al. 2014; Cox et al. 2016; Panopoulou et al. 2016). In fact, a variety of orientations have previously been observed, and the alignment of filaments and apparent field direction appear to vary between regions and for different gas densities (cf., Pillai 2017, for a review). For example, there is observational evidence that low-column density filaments, or “striations,” are aligned parallel to the magnetic field lines in Taurus, as they are more susceptible to the magnetic influence than higher column density structures (Palmeirim et al. 2013). These striations may therefore promote mass accretion onto larger filaments, in which stars are finally formed. Such studies, however, were mostly performed using lower-resolution dust column density maps obtained with Herschel (e.g., Li et al. 2013; Palmeirim et al. 2013; Soler et al. 2017) or polarization data tracing relatively low AV environments (e.g., Busquet et al. 2013; Fissel et al. 2016; Planck Collaboration Int. XXXV 2016; Soler et al. 2016; Jow et al. 2018). Nevertheless, as discussed in Section 4.3, the filament F2 studied in this work has molecular hydrogen column densities roughly 2 to 3 orders of magnitude larger than the Herschel filaments studied in Cygnus or Taurus, and may therefore possibly be self-gravitating. At face value, these results are at odds with the widely accepted paradigm of magnetically regulated star formation in filaments, in which a perpendicular orientation of dense filaments with respect to the magnetic field is expected (e.g., Peretto et al. 2012; Busquet et al. 2013; Palmeirim et al. 2013; André et al. 2014; Cox et al. 2016; Panopoulou et al. 2016).


Statistical investigations of a large sample of photometric data are useful in exploring the bulk properties of various types of events. Nevertheless, such studies are generally limited to LCs in selected passbands, and also the follow-up covers a short duration of the SN evolution. Further, the spectroscopic follow-up of most objects is restricted to early phases. Nonuniformity in the data sample may also be present, as these are collected at different observing facilities and detectors. Studying individual events with proper monitoring at different phases (both photospheric and nebular) is hence extremely important. The very early phase observations (hours to days after explosion) of these events are useful to constrain the progenitor radius at its end stage. This needs a very early detection and quick follow-up, which is not always possible considering their random occurrence in the sky. The large-area surveys with high cadence have contributed significantly in this regard. SN 1993J (Type IIb) is the first event among SE-SNe that shows evidence of a prominent cooling tail just after explosion (Richmond et al. 1994; Barbon et al. 1995). During the past two decades, several Type IIb SNe with such interesting features have been monitored and studied well, e.g., SN 2008ax (Pastorello et al. 2008; Roming et al. 2009), SN 2011dh (Arcavi et al. 2011), SN 2011fu (Kumar et al. 2013; Morales-Garoffolo et al. 2015), SN 2013df (Fremling et al. 2014), and SN 2016gkg (Arcavi et al. 2017; Tartaglia et al. 2017). A handful of Type Ib events have also been discovered at very early phases, such as SN 1999ex (Hamuy et al. 2002; Stritzinger et al. 2002), SN 2008D (Mazzali et al. 2008; Soderberg et al. 2008; Malesani et al. 2009; Modjaz et al. 2009; Bersten et al. 2013), iPTF13bvn (Bersten et al. 2014; Fremling et al. 2016), and LSQ13abf (Stritzinger et al. 2020), and their observational properties are studied in detail. Recently, Prentice et al. (2019) analyzed the properties of 18 SE-SNe and discussed the implications for their progenitors. Direct detection of SE-SNe progenitor candidates has been possible only for a few objects, e.g., SN 1993J (Maund et al. 2004), SN 2011dh (Maund et al. 2011; Van Dyk et al. 2013), SN 2001ig (Ryder et al. 2018), iPTF13bvn (Cao et al. 2013; Eldridge et al. 2015; Kuncarayakti et al. 2015; Folatelli et al. 2016), SN 2016gkg (Kilpatrick et al. 2017; Tartaglia et al. 2017), SN 2017ein (Kilpatrick et al. 2018; Van Dyk et al. 2018; Xiang et al. 2019), and SN 2019yvr (Kilpatrick et al. 2021). Along with very early phases, the temporal observations during maximum to nebular phases are equally important to estimate various explosion parameters and progenitor properties. Detailed investigation of more events can provide an alternative way to understand various progenitor channels.


As a baseline for understanding how the scaling relations evolve as a function of mass and redshift, we adopt the following self-similar scalings:

(2)

\begin{equation}
M_{\rm {gas},\Delta }\propto M_{\Delta },
\end{equation}

(3)

\begin{equation}
T_{\Delta }\propto M^{2/3}_{\Delta }E^{2/3}(z),
\end{equation}

(4)

\begin{equation}
Y_{\rm {X},\Delta }\propto M_{\Delta }^{5/3}E^{2/3}(z),
\end{equation}

(5)

\begin{equation}
Y_{\rm {SZ},\Delta }\propto M_{\Delta }^{5/3}E^{2/3}(z),
\end{equation}

(6)

\begin{equation}
L_{\Delta }^{\rm {X,bol}}\propto M^{4/3}_{\Delta }E^{7/3}(z),
\end{equation}

(7)

\begin{equation}
L_{\Delta }^{\rm {X,bol}}\propto T^{2}E(z),
\end{equation}

where $E(z)\equiv H(z)/H_0=\sqrt{\Omega _{\text{m}}(1+z)^3+\Omega _{\Lambda }}$, Δ is the chosen overdensity relative to the critical density and YX is the X-ray analogue of the integrated SZ effect. These are derived in Appendix B. Although shown to be too simplistic by the first X-ray studies of clusters (Mushotzky 1984; Edge & Stewart 1991; David et al. 1993), the self-similar relations allow us to investigate if astrophysical processes are less significant in more massive clusters or at higher redshift. To enable a comparison with the self-similar predictions, and previous work, we fit the scaling relations of our samples at each redshift. We derive a median relation by first binning the clusters into bins of log mass (width: 0.1 dex) or log temperature (width: 0.07 dex) and then computing the median in each bin with more than 10 clusters. We also remove the evolution in normalization predicted by self-similar relations. The medians of the bins are then fitted with a power law of the form

(8)

\begin{equation}
E^{\beta }(z)Y=10^A\left(\frac{X}{X_0}\right)^{\alpha }\!\!,
\end{equation}

where A and α describe the normalization and slope of the best fit, respectively, β removes the expected self-similar evolution with redshift, X is either the total mass or temperature and Y is the observable quantity (Mgas, LX, bol, etc.). X0 is the pivot point, which we set to 4 × 1014 M⊙ for observable–mass relations and to 6 keV for observable–temperature relations. We note that we fix the pivot for all samples and all redshifts. Fitting to the medians of bins, rather than individual clusters, prevents the fit from being dominated by low-mass objects, which are significantly more abundant due to the shape of the mass function. For the hot sample and its relaxed subset, there are too few bins with 10 or more clusters to reliably derive a best-fitting relation at z ≥ 1. By limiting our sample to systems with M500 ≥ 1014 M⊙, we avoid any breaks in the power-law relations that have been seen both observationally and in previous simulation work (Le Brun et al. 2016).


NASA’s Kepler mission has unveiled a wealth of new planetary systems (e.g. Borucki et al. 2010). These systems offer new insights into the process of planet formation and evolution. One of Kepler’s key findings is that the most common planets in our Galaxy, observed to date, are between 1 and 4 R⊕, i.e. larger than Earth but smaller than Neptune (Fressin et al. 2013; Petigura, Marcy & Howard 2013). Further observations revealed a transition in average densities at planet sizes ∼1.5 R⊕ (Marcy et al. 2014; Rogers 2015), with smaller planets having densities consistent with rocky compositions while larger planets having lower densities indicating significant H/He envelopes. In addition, Owen & Wu (2013) noticed a bimodal distribution of observed planet radii. Since then, refined measurements have provided strong observational evidence for the sparseness of short-period planets in the size range of ∼1.5–2.0 R⊕ relative to the smaller and larger planets, yielding a valley in the small exoplanet radius distribution (e.g. Fulton et al. 2017; Fulton & Petigura 2018). For example, the California-Kepler Survey reported measurements from a large sample of 2025 planets, detecting a factor of ∼2 deficit in the relative occurrence of planets with sizes ∼1.5–2.0 R⊕ (Fulton et al. 2017). Studies suggest that this valley likely marks the transition from the smaller rocky planets: ‘super-Earths’, to planets with significant H/He envelopes typically containing a few per cent of the planet’s total mass: ‘sub-Neptunes’ (e.g. Lopez & Fortney 2013, 2014; Owen & Wu 2013; Rogers 2015; Ginzburg, Schlichting & Sari 2016). Furthermore, the location of this valley is observed to decrease to smaller planet radii, Rp, with increasing orbital period, P. In a recent study involving asteroseismology-based high precision stellar parameter measurements for a sample of 117 planets, a slope $\text{d log} R_\mathrm{ p}/ \text{d log} P = -0.09^{+0.02}_{-0.04}$ was reported for the radius valley by Van Eylen et al. (2018). A similar value for the slope of $-0.11^{+0.03}_{-0.03}$ was reported by Martinez et al. (2019).


There are multiple sources of spurious detections in strong lensing analysis, and some of them are just image artefacts. None the less, others are images that might look like a lens, as in the case of edge-on galaxies and some spiral galaxies. One popular approach to reduce this issue is to use colours and look for red galaxies with a blue object close. The colour information is considered an important feature to find lenses (see e.g. Ostrovski et al. 2018; Spiniello et al. 2018). The use of colour queries is considered at least an interesting way to make a pre-selection and improve the final deep learning result purity. In fact, even in the I SGLC results the single-band scenario found lower performance in terms of the considered metric, the AUC of ROC. The II SGLC in a Euclid-like scenario allowed to make deep learning classifications using images with colour information in multiple bands but also higher resolution images. Interestingly the networks using only HJY bands did not find a competitive fit. On the other hand, using the VIS band only with a higher resolution found better results than HJY, which is close to a random guess. Still, the VIS band only has inferior results if compared to the runs using all information. This result suggests that the use of high-resolution images might play an important role and was in fact, more relevant than the multiple bands in the cases tested in this contribution. It is worth mentioning that the threshold defined by the maximum Fβ, with β = 0.001, privileged a pure sample instead of a complete one, obtaining in our best network, HJY + VIS with alternative pre-processing, around ${\sim} 99{{\ \rm per\ cent}}$ purity with completeness of around $45{{\ \rm per\ cent}}$. This choice is justified for the same reason a pre-selection of targets is implemented by colour queries and other methods to reduce the number of non-lenses in a survey where we have billions of non-lenses for thousands of lenses.


We present these results with one very important caveat. Accurately determining metallicities at different redshifts is of key importance to studying the evolution of the MZR. In the local universe, relationships between strong emission line ratios and metallicity can be calibrated to âdirectâ electron temperature-determined metallicities from measuring auroral lines such as [Oâiii]Â Î»4363 (Pettini & Pagel 2004; Pilyugin & Thuan 2005) or photoionization models of star-forming regions (Zaritsky et al. 1994; Kewley & Dopita 2002; Kobulnicky & Kewley 2004; Tremonti et al. 2004). At redshifts above z â¼ 1, it is nearly impossible to detect weak auroral lines for directly determining metallicity (but see Yuan & Kewley 2009; Rigby et al. 2011; Brammer et al. 2012a; Christensen et al. 2012; Maseda et al. 2014). Creating photoionization models that suitably represent high-redshift star-forming regions requires knowledge of physical parameters which have been poorly constrained up to this point. Thus, it is unknown if local metallicity calibrations hold at high redshifts. Figure 6 shows a comparison between metallicities determined using the O3N2 indicator and the N2 indicator for both local SDSS galaxies (grey points) and MOSDEF z â¼ 2.3 galaxies (black points). The black dashed line indicates a one-to-one relationship. If local calibrations do indeed hold at high redshifts, then the relationship between metallicities determined from different indicators should not evolve with redshift. It is clear that the z â¼ 2.3 galaxies are offset below the local galaxies. The dotted line is the best-fit line of slope unity to the individual z â¼ 2.3 galaxies, yielding an offset of â0.1 dex from a one-to-one correspondence, over twice that displayed by the SDSS sample. Steidel et al. (2014) found an offset slightly larger than this at z â¼ 2.3. This offset demonstrates that the two metallicity indicators are not evolving in the same way with redshift, and shows the need of metallicity calibrations appropriate for high-redshift galaxies.


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).


On balance, a large abundance of baryon-dominated, dark matter cored galaxies at z ∼ 2, most strongly correlated with baryonic surface density, angular momentum, and central bulge mass, may be most naturally accounted for by the interaction of baryons and dark matter during the formation epoch of massive halos. Massive halos (log(Mhalo/M⊙) > 12) formed for the first time in large abundances in the redshift range z ∼ 1–3 (Press & Schechter 1974; Sheth & Tormen 1999; Mo & White 2002; Springel et al. 2005). At the same time, gas accretion rates were maximal (Tacconi et al. 2020). This resulted in high merger rates (Genel et al. 2008, 2009; Fakhouri & Ma 2009), very efficient baryonic angular momentum transport (Dekel et al. 2009; Zolotov et al. 2015), formation of globally unstable disks, and radial gas transport by dynamical friction (Noguchi 1999; Immeli et al. 2004; Genzel et al. 2008; Bournaud & Elmegreen 2009; Bournaud et al. 2014; Dekel & Burkert 2014). These processes enabled galaxy mass doubling on a timescale  0.4 Gyr at z ∼ 2–3, and massive bulge formation by disk instabilities and compaction events on 1 Gyr timescales. However, central baryonic concentrations would naturally also increase central dark matter densities through adiabatic contraction (Barnes & White 1984; Blumenthal et al. 1986; Jesseit et al. 2002). For adiabatic contraction to be ineffective requires the combination of kinetic heating of the central dark matter cusp by dynamical friction from in-streaming baryonic clumps (El-Zant et al. 2001; Goerdt et al. 2010; Cole et al. 2011), with feedback from winds, supernovae, and AGNs driving baryons and dark matter out again (Dekel & Silk 1986; Pontzen & Governato 2012, 2014; Martizzi et al. 2013; Freundlich et al. 2020; K. Dolag et al. 2020, in preparation). Using idealized Monte Carlo simulations, El-Zant et al. (2001) demonstrated that dynamical friction acting on in-spiraling gas clumps can provide enough energy to heat up the central dark matter component and create a finite dark matter core (see also A. Burkert et al. 2020, in preparation). They argue that dark matter core formation in massive galaxies would require that clumps be compact, such that they avoid tidal and ram-pressure disruption, and have masses of >108 M⊙. Other idealized simulations (e.g., Tonini et al. 2006) confirm these results.


It is worth noting that more ECSNe are predicted for systems with a mass ratio close to unity, as the development of contact happens at longer periods for higher-q systems (see Figure 12). As the primary star starts transferring mass to the secondary, the orbit shrinks, until the mass ratio is reversed. This reversal happens earlier for mass ratios close to 1 and later for lower mass ratios, increasing the chance for contact (de Mink et al. 2013). This primarily affects the numbers of ECSNe, not so much the initial mass range (except for Case A systems). This is also true for the value of β, which controls the amount of matter that is lost from the system (i.e.,

 is conservative mass transfer, no mass that is transferred from the primary to the secondary is lost from the system;

 is completely nonconservative mass transfer, all mass that is transferred from the primary is lost from the system, no accretion onto the secondary). For the sake of our parameter study we chose various fixed values of β, while the mass transfer efficiency in real systems varies in time and will depend on the evolutionary phase of both stars, the amount of matter already accreted onto the secondary, and how that has affected its spin rate. Several mechanisms have been suggested that control the efficiency of mass transfer, mass accretion, and mass loss, including the existence of an accretion disk that regulates the amount of mass and angular momentum that can be accreted (Paczyński 1991; Popham & Narayan 1991; Deschamps et al. 2013), the necessity of the secondary to stay below critical rotation (Packet 1981), and the effects of tides on the stellar spins and the stellar orbit (Zahn 1977; Hurley et al. 2002). Work by Deschamps et al. (2013) and van Rensbergen et al. (2008) for systems with slightly lower masses suggests periods with values of β close to 1 (i.e., very inefficient mass transfer), while simulations with a strong tidal interaction (i.e., a short spin–orbit synchronization timescale) suggest shorter periods of moderately inefficient mass transfer (Paxton et al. 2015). Although our models suggest that the efficiency of mass transfer does not really affect the mass range for ECSNe, it does, however, strongly affect the range of initial periods that can lead to an ECSN. In addition, the evolution of the secondary will be affected. It will most likely rapidly spin up after the onset of mass transfer and maintain near-critical rotation for possibly extended periods of time. This will induce strong rotational mixing (de Mink et al. 2008a, 2013; Langer 2012), causing possibly quasi-chemically homogeneous evolution (Maeder 1987; Langer 2012), and alter the evolution of the star beyond just the simple fact of mass accretion (Hirschi et al. 2004; de Mink & Mandel 2016; Marchant et al. 2016). Although it is not clear to what extent this will affect the incidence of ECSNe, the effects of tides, mass and angular momentum transfer and loss, and near-critical rotation of the secondary are possibly important and will be discussed in a forthcoming paper, in addition to the effects of additional mixing and convection criteria.


In Fig. 11, we present the evolution of the quasar bolometric luminosity function (LF) from $z \sim 3$ down $z \sim 0$. Even though these functions give the number density of accreting black holes in different luminosity bins, they have been a powerful tool to extract information on how MBHs grow with cosmic time, on the geometry of the accretion discs and other fundamental quantities such as the black hole spins and radiative efficiencies. In this work, we only focus on the very bright objects, i.e. ${\gt}10^{45}\, \rm erg\,s^{-1}$, avoiding the comparison with lower luminosity given the current limitations on observational and theoretical models. In particular, from an observational standpoint, the covered area and depth of current surveys pose serious challenges when extracting statistical properties of the LF at the faint end (Siana et al. 2008; Masters et al. 2012; McGreer et al. 2013; Niida et al. 2016; Akiyama et al. 2018). Even more, dust attenuation effects might play an important role in shaping current measurements. On the other hand, current theoretical works show a large excesses at luminosity ${\lt}10^{45}\, \rm erg\,s^{-1}$. In order to reconcile observations with predictions, these works have played with empirical relations for obscuring accreting black holes or with the efficiency of the seeding process (see e.g Degraf, Di Matteo & Springel 2010; Fanidakis et al. 2012; DeGraf & Sijacki 2020). Even though these works provide interesting results shedding light on the nature of low-luminous quasars, the treatment of seeding or dust obscuration is beyond the scope of this paper. As shown in Fig. 11, the fiducial model is compatible with current observations of the quasar LF, showing a sharp cut-off at larger luminosity (Shen et al. 2020). On the other hand, the models with higher gas accretion display a completely different behaviour. Boosting the gas accretion during DI leads to a larger excess of bright quasars at $z \gt 1.0$. For instance, at $z \sim 2$ and for luminosities ${\gt}10^{46}\, \rm erg\,s^{-1}$, the models with $A_{\rm yr^{-1}} \sim 1.92 \times 10^{-15}$ and $A_{\rm yr^{-1}} \sim 2.67 \times 10^{-15}$ are systematically overpredicting the number density by a factor of ${\sim }1$ and ${\sim }2\, \rm dex$, respectively. A similar behaviour is seen at $z \sim 3$. At lower redshifts ($z \lt 1.0$), the model follows both the fiducial results and the observed trends. This is principally caused by the decrease of important DIs events at these redshifts. Regarding the IM models, we can see similar trends at $z \gt 2$, where the bright end of the LF is systematically larger than the observed one. We highlight that the difference is larger with $A_{\rm yr^{-1}} \sim 2.67 \times 10^{-15}$. Interestingly, the excess with respect to the observations is smaller than with the IDI model. This is principally caused by the fact that DI events are more important than mergers at these redshifts (Izquierdo-Villalba et al. 2020). At lower redshifts, we can see larger differences with respect to the fiducial and the IDI models: IM model is systematically overprotecting the bright end of the LF (${\gt}10^{46}\, \rm erg\,s^{-1}$). Such differences can be a factor of 3 (1.5) by $z \sim 0$ up to a factor of 5 (2) at $z \sim 0.5$ for $A_{\rm yr^{-1}} \sim 1.92 \times 10^{-15}$ ($A_{\rm yr^{-1}} \sim 2.67 \times 10^{-15}$).


The gas particles are distributed such that the initial surface density profile has the power law:
(7)$$\begin{eqnarray*}
\Sigma _\mathrm{g}=\Sigma _{\mathrm{g},0}\left(\frac{R}{R_{\rm in}}\right)^{-p},
\end{eqnarray*}$$where Σg, 0 is a normalization constant. We choose a locally isothermal equation of state with a sound speed cs such that
(8)$$\begin{eqnarray*}
c_\mathrm{s} = c_\mathrm{s,in} \left(\frac{R}{R_{\rm in}} \right)^{-q}.
\end{eqnarray*}$$where cs, in is the sound speed at the inner radius. We choose power law indices p = 1 and q = 0.5. We use a constant SPH artificial viscosity coefficient αSPH ≈ 0.55 such that the corresponding disc viscosity coefficient (Shakura & Sunyaev 1973) is αd = 0.01 at the inner radius. We note, however, that our choice of the indices p and q impact the radial dependence of the viscosity coefficient αd. The mapping between the SPH and disc viscosity coefficient is (e.g. Artymowicz & Lubow 1994, Murray 1996, Lodato & Pringle 2007)
(9)$$\begin{eqnarray*}
\alpha _\mathrm{d} \propto \alpha _{\rm SPH} \frac{\langle h \rangle }{H}
,
\end{eqnarray*}$$where 〈h〉 is the vertically averaged smoothing length and H is the disc thickness, which depends on disc radius:
(10)$$\begin{eqnarray*}
H = \frac{c_\mathrm{s}}{\Omega _\mathrm{k}} \propto R^{3/2-q}
.
\end{eqnarray*}$$The average smoothing length in 3D has the radial dependence:
(11)$$\begin{eqnarray*}
\langle h \rangle \propto \rho ^{-1/3} \propto \left(\frac{\Sigma }{H}\right)^{-1/3} \propto R^{1/2+(p-q)/3}
.
\end{eqnarray*}$$Hence, the choice of p and q determines the radial dependence of the disc viscosity coefficient. A common choice is to use p = 3/2 and q = 3/4, which results in a radially constant αd (e.g. Lodato & Price 2010). In this paper, we decide to use a physically motivated choice of indices (e.g. Aly et al. 2018), even though it results in a non-constant disc viscosity coefficient. For our choice of p = 1 and q = 0.5, the resulting radial dependence of the disc viscosity coefficient is αd ∝ R−1/3. To prevent particle interpenetration, we employ the recommended value βSPH = 2 for the quadratic viscosity coefficient (Price & Federrath 2010; Meru & Bate 2012).


A prime example of the unique benefits of the intersection of the 3D nature of (ultra)hot exoplanet atmospheres and high-resolution spectroscopy lies in the ultrahot Jupiter WASP-76b, a gas giant orbiting an F7 star (West et al. 2016) that is well studied at lower resolution (Fu et al. 2017, 2021; Fisher & Heng 2018; Tsiaras et al. 2018; Edwards et al. 2020; von Essen et al. 2020). Using the high-resolution (R ≈ 138,000) ESPRESSO spectrograph on the Very Large Telescope (Pepe et al. 2010, 2013), the Ehrenreich et al. (2020) team was able to produce novel, high signal-to-noise ratio (S/N), phase-resolved transmission spectra of this target across two separate transits. Curiously, an anomalous Doppler signature in the planet’s transmission spectrum was detected: between 0 and −5 km s−1 at ingress, but roughly −11 km s−1 by egress. These speeds far exceed the few km s−1 planet-frame velocities detected on other planets (Snellen et al. 2010; Brogi et al. 2016). The detection team attributes this variable and strong blueshift to an asymmetric distribution of atomic iron in the planet’s atmosphere, with a considerable amount of iron existing in the gas phase east of the substellar point, but cooling and condensing out as it makes its way to the much colder nightside. This asymmetry would cause a progressively blueshifted signal as the gas-phase iron region rotates into view over the course of the planet’s transit. Ehrenreich et al. (2020) posit that their signal is composed of two independent Doppler components: solid-body rotation of ±5.3 km s−1  (the tidally locked equatorial velocity of the planet), and a uniform day–night wind contributing an additional −5.3 km s−1 across both limbs, with gas-phase iron only present on the evening limb of the planet (Figure 1). Hence, the approaching limb would exhibit a blueshift from both rotation and winds totaling –10.6 km s−1, and the receding limb would produce no Doppler signal, as it would contain no gas-phase iron to absorb starlight.


We study here the Sco OB1 association (Figs. 1 and 2), using this and other techniques. The general properties of this large OB association, which spans almost 5° on the sky, and is surrounded by a ring-shaped HII region called Gum 55, are reviewed by Reipurth (2008). Its central cluster NGC 6231 contains several tens of OB stars, which have been extensively studied. On the other hand, many fewer studies, all recent, were devoted to the full mass spectrum, using optical photometry (Sung et al. 1998, 2013) and X-rays (Sana et al. 2006, 2007; Damiani et al. 2016; Kuhn et al. 2017a,b). The currently accepted distance of NGC 6231 is approximately 1580 pc, and its age is between 2and 8 Myr, with a significant intrinsic spread (Sung et al. 2013; Damiani et al. 2016). No ongoing star formation is known to occur therein, however. Approximately one degree North of the cluster, the loose cluster Trumpler 24 (Tr 24) also belongs to the association. There is little literature on this cluster (Seggewiss 1968; Heske & Wendker 1984, 1985; Fu et al. 2003, 2005) which unlike NGC 6231 lacks a well-defined center and covers about one square degree on the sky. Its age is  10 Myr according toHeske & Wendker (1984, 1985), who find several PMS stars, and its distance is 1570–1630 pc according to Seggewiss (1968). Other studies of the entire Sco OB1 association include MacConnell & Perry (1969 – Hα-emission stars), Schild et al. (1969 – spectroscopy), Crawford et al. (1971 – photometry), Laval (Laval 1972a,b – gas and star kinematics, respectively), van Genderen et al. (1984 – Walraven photometry), and Perry et al.(1991 – photometry). At the northern extreme of Sco OB1, the partially obscured HII region G345.45+1.50 and its less obscured neighbor IC4628 were studied by Laval (1972a), Caswell & Haynes (1987), López et al. (2011), and López-Calderón et al. (2016). They contain massive young stellar objects (YSOs; Mottram et al. 2007), maser sources (Avison et al. 2016), and the IRAS source 16562-3959 with its radio jet (Guzmán et al. 2010), outflow (Guzmán et al. 2011), and ionized wind (Guzmán et al. 2014), and are therefore extremely young (1 Myr or less). The distance of G345.45+1.50 was estimated as 1.9 kpc by Caswell & Haynes (1987), and 1.7 kpc by López et al. (2011), in fair agreement with distances of Sco OB1 stars. In Fig. 1 of Reipurth (2008) a strip of blue stars is visible, connecting NGC 6231 to the region of IC4628.


In an attempt to understand the radio properties of GRBs, Chandra & Frail (2012) conducted a complete investigation of all historical events observed in the radio domain. These included both of the main GRB populations (Kouveliotou et al. 1993): long-duration GRBs (likely produced by massive stellar collapse where the gamma-ray emission lasts for more than 2 s; Woosley 1993; Kulkarni et al. 1998; Woosley & Bloom 2006) and short-duration GRBs (likely caused by the coalescence of two neutron stars or a neutron star and black hole, which lasts for less than 2 s; Lattimer & Schramm 1976; Eichler et al. 1989; Narayan, Paczynski & Piran 1992). Only 30 per cent of their sample had a detectable radio afterglow, with the radio emission peaking within a very narrow flux range. This led them to conclude that the low percentage of detections was likely due to the sensitivity of radio telescopes rather than there being two distinct GRB populations: radio-bright and radio-faint. Ghirlanda et al. (2013) and Burlon et al. (2015) then conducted simulations to demonstrate that potentially all Swift GRBs will be detectable at radio frequencies with phase 1 of the Square Kilometre Array (SKA), specifically SKA1-MID in Band 5 (∼9 GHz)1 between 2 and 10  d post-burst, as well as with the recently upgraded Karl G. Jansky Very Large Array (VLA)2 and MeerKAT (the South African SKA precursor telescope; Jonas 2009). In fact, SKA1-MID will be so sensitive it could detect the radio counterparts from GRBs with gamma-ray emission up to five times fainter than those currently detected with Swift-BAT (note that these simulations do not account for radio emission produced by the reverse-shock, only considering contributions from the forward-shock; Burlon et al. 2015). However, a study conducted by Hancock, Gaensler & Murphy (2013), which involved visibility stacking of VLA GRB radio observations, suggested the low radio detection rate may be due to there being separate radio-bright and radio-faint GRB populations, and that ≤70 per cent are likely to be truly radio bright.


Moreover, there could also be some contribution to the detected temperature asymmetry from high-latitude gas clouds in our Galaxy along the line of sight toward M 81. In this respect we note that M 81 is at about 40.9° north of the Galactic disk, where contamination from the Milky Way is expected to be low. However, interpretation of astronomical observations is often hampered by the lack of direct distance information. Indeed, it is often not easy to judge whether objects on the same line of sight are physically related or not. Since the discovery of the Arp’s Loop (Arp 1965) the nature of the interstellar clouds in this region has been debated; in particular whether they are related to the tidal arms around the galaxy triplet (Sun et al. 2005; de Mello et al. 2008) or to Galactic foreground cirrus (Sollima et al. 2010; Davies et al. 2010). Already Sandage et al. (1976) presented evidence showing that we are observing the M 81 triplet through widespread Galactic foreground cirrus clouds and de Vries et al. 1987 built large-scale HI, CO, and dust maps that showed Galactic cirrus emission toward the M 81 region with NH ≃ 1−2  ×  1020 cm-2. The technique used to distinguish between the emission from extragalactic or Galactic gas and dust relies on spectral measurements and on the identification of the line of sight velocities, which are expected to be different in each case. Unfortunately, in the case of the M 81 Group, this technique appears hardly applicable since the radial velocities of extragalactic and Galactic clouds share a similar LSR (local standard of rest) velocity range (Heithausen 2012). Several small-area molecular clouds (SAMS), that is, tiny molecular clouds in a region where the shielding of the interstellar radiation field is too low (so that these clouds cannot survive for a long time), have been detected by Heithausen (2002) toward the M 81 Group. More recently, data from the Spectral and Photometric Imaging Receiver (SPIRE) instrument onboard Herschel ESA space observatory and Multiband Imaging Photometer for Spitzer (MIPS) onboard Spitzer allowed the identification of several dust clouds north of the M 81 galaxy with a total hydrogen column density in the range 1.5–5 × 1020 cm-2 and dust temperatures between 13 and 17 K (Heithausen 2012). However, since there is no obvious difference among the individual clouds, there was no way to distinguish between Galactic or extragalactic origin although it is likely that some of the IR emission both toward M 81 and NGC 3077 is of Galactic origin. Temperature asymmetry studies in Planck data may be indicative of the bulk dynamics in the observed region provided that other Local (Galactic) contamination in the data is identified and subtracted. This is not always possible, as in the case of the M 81 Group, and therefore it would be important to identify and study other examples of dust clouds where their origin, either Galactic or extragalactic, is not clear. One such example might be provided by the interacting system toward NGC 4435/4438 (Cortese et al. 2010) where the SAMS found appear more consistent with Galactic cirrus clouds than with extragalactic molecular complexes. Incidentally, the region A1 within R0.50 has been studied by Barker et al. (2009), who found evidence for the presence of an extended structural component beyond the M 81 optical disk, with a much flatter surface brightness profile, which might contain ≃10–15% of the M 81 total V-band luminosity. However, the lack of both a similar analysis in the other quadrants (and at larger distances from the M 81 center) and the study of the gas and dust component associated to this evolved stellar population, hamper our understanding of whether this component may explain the observed temperature asymmetry toward the M 81 halo.


Alongside studies of their emission properties, examining the environments of FRBs on subparcsec to kiloparsec scales can be equally informative. Thus far, only a fraction of known extragalactic repeating FRBs have been localized to host galaxies (Chatterjee et al. 2017; Tendulkar et al. 2017; Marcote et al. 2020; Macquart et al. 2020; Heintz et al. 2020; Bhandari et al. 2021; Bhardwaj et al. 2021a, 2021b; Li et al. 2021).17

17
FRB 20201124A is the fifth announced repeating FRB with a host galaxy. In total, there are eight such FRB sources known as of 2021 August.
 Much closer by, FRB-like emission has been detected from a Milky Way magnetar, SGR 1935 + 2154 (CHIME/FRB Collaboration et al. 2020; Bochenek et al. 2020). All identified repeating FRB hosts have evidence for low to modest ongoing star formation rates of ∼0.06−2 M⊙ yr−1 (Gordon et al. 2004; Bhandari et al. 2020a; Heintz et al. 2020), several exhibit spiral arm morphologies (Bhardwaj et al. 2021a; Mannings et al. 2021; Tendulkar et al. 2021), and their stellar populations span a range of stellar masses, ∼108−1010.5 M⊙ (de Blok et al. 2008; Bhandari et al. 2020a; Heintz et al. 2020; Mannings et al. 2021). At face value, these characteristics, coupled with the absence of any quiescent host galaxy identifications for repeating FRBs, may indicate that FRBs are connected to host galaxies with ongoing star formation. However, studies of their more local environments reveal a rich diversity. For instance, the discovery of the repeating FRB 20200120E in an old (9.1 Gyr) globular cluster on the outskirts of the grand design spiral galaxy M81 (Bhardwaj et al. 2021a; Kirsten et al. 2021a) and the detection of the repeating FRB 20121102A embedded in a star-forming knot in its dwarf host galaxy (Bassa et al. 2017) seemingly represent polar opposite local environments. If all repeaters discovered to date originate from the same type of progenitor, then the progenitor model must accommodate the observed diversity of both local and galactic environments. Additionally, any connection in progenitors to the population of apparent nonrepeaters remains opaque.


After checking the results of the U-net in the test area, we applied the same detection method to the full-sky SZ prediction map, with a detection threshold of pmax = 0.1 in order to recover the maximum number of Planck_z clusters. We detected 20 204 sources in the full-sky map with the U-net with pmax = 0.1. We compared the detections with the three catalogues of known galaxy clusters, Planck_z, Planck_no-z, and MCXCwP. Among the 20 204 detected sources, 98.5% of the Planck_z clusters are recovered, together with 76.4% of Planck_no-z clusters, and 20.8% of MCXCwP clusters. Moreover, 11 cluster are identified by ACT (Hasselfield et al. 2013) and 98 clusters are identified by the South Pole Telescope (SPT; Bleem et al. 2015), but are not included in the Planck PSZ2 catalogue. This means that 18 415 sources do not belong to any of the catalogues. We investigated the nature of the sources detected with the U-net. First, we cross-matched the sample of 18 415 sources with Planck point sources. Only 6.1% are matched within a cross-match radius of 5 arcmin with the positions of the Planck catalogue of galactic cold cores, and only 0.2% are matched with the positions of the Planck sources identified at 353 GHz. Second, we stacked at their positions 16 maps in different wavelengths, each of them potentially probing different galaxy cluster counterparts. Some of the 16 maps are also based on Planck data and thus are not independent, but some of the maps are independent and may show indications of galaxy cluster counterparts in other wavelengths, that is, in near-infra-red (where galaxies emit) and in X-rays (where the same gas emits as is detected with the SZ). The 16 maps are the Planck SZ MILCA map, the 6 Planck HFI frequency maps, the IRIS map at 100 μm (Miville-Deschênes & Lagache 2005), the CMB lensing map (based on Planck; Planck Collaboration VIII 2020), 4 galaxy over-density maps of all galaxies (called GAL ALL), passive galaxies (called GAL P), transitioning galaxies (called GAL T), and active galaxies (called GAL A) following Bonjean et al. (2019), star formation rate density maps (called SFR), a stellar mass density maps (called Mstar) constructed with the method from Bonjean et al. (2019), and finally, the ROSAT X-ray map (ByoPiC4 product). The Planck maps were masked from the Planck Catalogue of Compact Sources (PCCS, Planck Collaboration XXVI 2016), and the ROSAT map was masked from the point sources detected in ROSAT, Chandra, and XMM-Newton (Boller et al. 2016; Evans et al. 2010; Rosen et al. 2016, respectively). The result of the 16 stacks are shown in Fig. 5.


Among the most viable mechanisms of cusp-core transformation that require changes to the assumed cosmogony is one that was proposed specifically as a possible solution to the cusp-core problem. It proposes that the DM is in fact not collisionless but self-interacting (SIDM; Spergel & Steinhardt 2000; Yoshida et al. 2000; Davé et al. 2001; Colín et al. 2002; Vogelsberger, Zavala & Loeb 2012; Rocha et al. 2013; see Tulin & Yu 2018 for a review). In SIDM, particles can exchange energy and momentum through elastic scattering, causing an outside-in energy redistribution within the centre of DM haloes, resulting in the formation of an isothermal core. The time-scale on which an initially cuspy SIDM halo forms a flat and isothermal core is roughly given by the time it takes for each DM particle in the inner halo to scatter at least once (Vogelsberger et al. 2012; Rocha et al. 2013). The strength of the self-interaction in SIDM models is parametrized in terms of the momentum transfer cross-section per unit mass, σT/mχ. Depending on the specific SIDM model, σT/mχ can either be constant or dependent on the relative velocity between the two scattering DM particles. SIDM is an efficient mechanism of cusp-core transformation in dwarf-size haloes for $\sigma _T/m_\chi \gtrsim 1\, {\rm cm^2g^{-1}}$, whereas SIDM haloes are virtually indistinguishable from CDM haloes if $\sigma _T/m_\chi \lesssim 0.1\, {\rm cm^2g^{-1}}$ (Zavala, Vogelsberger & Walker 2013). The most stringent and precise constraints on the self-interaction cross-section have been put on the scales of galaxy clusters (e.g. Robertson, Massey & Eke 2017; Robertson et al. 2019) and large elliptical galaxies (Peter et al. 2013), where observations require that $\sigma _T/m_chi \lesssim 1\, {\rm cm^2g^{-1}}$. On smaller scales, Read, Walker & Steger (2018) concluded that $\sigma _T/m_\chi \lesssim 0.6\, {\rm cm^2g^{-1}}$, based on their findings that the central density profile of the MW dwarf spheroidal galaxy Draco is cuspy (see also the SIDM results of Valli & Yu 2018). Moreover, based on a DM only analysis of the updated too-big-to-fail problem, Zavala et al. (2019) concluded that SIDM models with a constant cross-section of $\sigma _T/m_\chi \sim 1\, {\rm cm^2g^{-1}}$ fail to explain the apparently large central densities of the host haloes of the ultra-faint satellites of the MW (Errani, Peñarrubia & Walker 2018). It should be pointed out that the constraints on σT/mχ on the scale of dwarf galaxies are affected by significantly larger systematic uncertainties than on the scales of galaxy clusters or elliptical galaxies. Moreover, Zavala et al. (2019) demonstrate that SIDM with a strongly velocity-dependent self-interaction cross-section may provide a natural explanation for the observed diversity in the rotation curves of the MW dwarf spheroidals (see also Correa 2021). The strong dependence of the self-interaction cross-section on the typical DM velocities would create a bimodal distribution of rotation curves in the MW satellites in which the heavier haloes have constant density cores while the lighter haloes have undergone gravothermal collapse and have very steep central cusps as a consequence. The same mechanism of gravothermal collapse might be accelerated by tidal interactions in the environment of the MW leading to an agreement between constant cross-section SIDM models with $\sigma _T/m_\chi \sim 3\, {\rm cm^2g^{-1}}$ and the internal kinematics of MW satellites (e.g. Kahlhoefer et al. 2019; Sameie et al. 2020).


In previous work (Rogers & Safi-Harb 2016, hereafter RSH16), we studied a parametrized phenomenological model for describing magnetic field growth in neutron star (NS) evolution. Assuming that the external dipole field is buried by an intense process of fall-back accretion after the formation of the NS (Muslimov & Page 1995, 1996), the slow diffusion from the stellar surface exerts a time-dependent torque on the NS (Geppert, Page & Zannias 1999). This process may provide an explanation for the observed braking indices of young pulsars with n ≠ 3 (Espinoza 2012; Archibald et al. 2016), in contrast to the prediction of a rotating magnetic dipole in vacuum (n = 3; Ostriker & Gunn 1969). Magnetic field growth provides an evolutionary link between apparently disparate classes of NS, such as the extremely weak field1 subset of NSs known as central compact objects (CCOs), high magnetic field pulsars (HBPs) and the X-ray dim isolated NSs (XDINSs). On the other hand, magnetic field decay has been invoked to describe the evolution of the anomalous X-ray pulsars (AXPs) and soft gamma repeaters (SGRs). These objects are conventionally described by the magnetar model that posits the development of large magnetic fields via dynamo action from rapid rotation early in the life of the NS, necessary to support the core of the massive progenitor from collapse (Duncan & Thompson 1992; Akiyama et al. 2003; Thompson, Quataert & Burrows 2005). However, the distinction between the apparently rotation-powered HBPs, with dipole fields just above the quantum critical limit, BQED = 4.4 × 1013 G, and the magnetically powered AXPs and SGRs (B ≥ BQED) is significantly blurred after the HBPs were observed displaying magnetar-like activity (Gavriil et al. 2008; Kumar & Safi-Harb 2008; Göğüs et al. 2016), and radio emission was observed from magnetars (Camilo et al. 2007). Moreover, the SNRs associated with the AXPs and SGRs show evidence of ‘typical’ explosion energies (≤1051 erg) and not superenergetic as one would expect from a rapidly rotating proto-NS (Vink & Kuiper 2006; Kumar, Safi-Harb & Gonzalez 2012; Safi-Harb & Kumar 2013; Kumar et al. 2014). These complications are difficult to reconcile with the standard magnetar picture.


To verify the predictions of CDM, there have been recent attempts to compare the amount of stellar mass observed in the outskirts of galaxies with the mass fraction in accreted stars predicted by simulations (e.g., Font et al. 2008; Pillepich et al. 2014; Merritt et al. 2016; D’Souza & Bell 2018; Elias et al. 2018; Huang et al. 2018). For the purposes of this paper, we will use the term “stellar halo” in an observational sense, referring to the faint structure in the outskirts of galaxies beyond the central concentration of stellar mass. In practice, there are many observational definitions of this term that can include radial profile, surface brightness, or metallicity characteristics. Most recent observational attempts to characterize the stellar halos of galaxies, including the MW (e.g., Carollo et al. 2010) and the Andromeda galaxy (M31; e.g., Courteau et al. 2011), have used analysis of resolved stellar populations to identify the accreted component, usually by searching for an old, metal-poor population extending far from the central galaxy (e.g., Seth et al. 2007; Cockcroft et al. 2013). However, this method requires extremely deep images, mainly obtained using the Hubble Space Telescope (with the exception of Greggio et al. 2014), and has therefore been limited to a small handful of galaxies so far. Observing stellar halos in integrated light is, in principle, more easily scalable to the sample sizes needed to explore the wide variation in the accreted component that is predicted by simulations (e.g., Bakos & Trujillo 2012; D’Souza et al. 2014; Duc et al. 2015; Merritt et al. 2016; Huang et al. 2018), presuming that it is possible to account for the contribution of scattered light (de Jong 2008; Slater et al. 2009; Sandin 2014). However, the lack of resolved stellar population information makes it far more challenging to identify the regions of an image dominated by accreted material. So far, no work has attempted to account for how the method used to select the stellar halo from a galaxy observed in integrated light may bias the comparison to simulations, where the provenance of material is perfectly known and a variety of definitions of “stellar halo” are imposed. Despite efforts such as that in Rodriguez-Gomez et al. (2016) to understand whether the mass in the stellar halo comes primarily from accreted material or from stars formed in the central galaxy (which in this work we call “formed in situ”) and expelled to the halo, and its dependence with separation from the central galaxy, it is not straightforward to apply these results to the spatial selections in projection that are commonly used in integrated-light images. In fact, most prior work has focused on comparisons between observed galaxies and predictions for the stellar halo based on dark matter–only (DM-only) simulations tagged with stars, where the stellar halo is by definition 100% accreted. However, in simulations that include baryonic physics, both of these distinct channels are observed to contribute to the stellar halos of galaxies (Font et al. 2011; Cooper et al. 2013; Tissera et al. 2013; Pillepich et al. 2015; Anglés-Alcázar et al. 2017; Gómez et al. 2017a), and both are interesting for what they tell us about the process of galaxy formation, as well as the cosmology in which galaxies are formed.


In the left-hand panel of Fig. 2, we compare the morphological types assigned by the STAGES collaboration for the galaxies in the whole OMEGA sample and the jellyfish candidates sample. The sample of jellyfish galaxies (JC345) is composed mainly of late-type spirals and irregulars. In the middle panel of Fig. 2, we show the distribution of SED types for both samples. Based on the SED types of the galaxies, of the 70 jellyfish galaxy candidates analysed, 66 were found to be part of the blue cloud and 4 as being dusty reds (IDs: 11633, 17155, 19108, 30604). However, contrary to what could be expected, dusty red galaxies are only a small portion of our sample of jellyfish candidates. One reason why we may not detect many dusty reds as jellyfish galaxies might be due to the fact that these galaxies, despite having relatively high SFRs (only four times lower than that in blue spirals at fixed mass, Wolf et al. 2009), have significant levels of obscuration by dust that might hamper the identification of the jellyfish signatures. Another reason for that is that we selected jellyfish galaxy candidates within a parent sample of H α-emitting galaxies that already had a low fraction of dusty red galaxies (≈15 ${{\ \rm per\ cent}}$). As these galaxies have low star formation, it is harder to perceive the morphological features of RPS. Dusty red galaxies have been previously studied in this same system (Wolf et al. 2009), and RPS was suggested to be the main mechanism acting in these galaxies (Bösch et al. 2013). While in Bösch et al. (2013) one of the main pieces of evidence suggesting the action of enhanced RPS were the existence of disturbed kinematics without disturbed morphologies, in our study we strongly base our selection on such morphological distortions. Both our jellyfish galaxy candidates and the dusty red galaxies show different characteristics that can be correlated to the effect of RPS. Nevertheless, they might be tracing different stages of the same phenomenon, where dusty red galaxies have more regular morphologies, but disturbed kinematics. Our sample of morphologically disturbed jellyfish galaxy candidates may be showing the stage where the features of RPS are the most visible and the SFRs are enhanced.


Over the last decade, the radial and vertical dependences of the metallicity-alpha-element distribution have been studied in more and more detail with increasingly larger samples (e.g., Bensby et al. 2011; Anders et al. 2014; Nidever et al. 2014; Hayden et al. 2015; Queiroz et al. 2020). Figure 6 is mostly consistent with similar plots shown in the above papers. In the inner 10 kpc, it displays two over-densities, a high alpha-element (here [Mg/Fe]), and a low one. Between Rg = 6 and 10 kpc, the two over-densities define two different sequences. In Appendix E, we note that when the sample is restricted to a ±500 pc layer around the Galactic plane, two close but separated sequences are observed in the Rg ∈ [4, 6] kpc interval. Because of their scale height (Bovy et al. 2012), kinematics (Bensby et al. 2003), and age properties (Haywood et al. 2013), these two sequences are associated with the thick disc (high-alpha) and thin disc (low-alpha), respectively. Moving inward of Rg = 4 − 6 kpc, Fig. 6 shows that the two over-densities connect through a zone of lower density to form a single sequence. This is in agreement with the observations of Hayden et al. (2015), Bensby et al. (2017), Zasowski et al. (2019), Bovy et al. (2019), and Lian et al. (2020a,b), who also report a single sequence in the inner disc and/or in the bulge/bar area. Conversely, Rojas-Arriagada et al. (2019) and Queiroz et al. (2020) observe two sequences in the inner regions. In Appendix F, we compare the distributions of different APOGEE DR16 alpha elements in the ([Fe/H], [α/Fe]) plane (restricting the sample to the stars contained in the Rg ∈ [0, 2] kpc interval). The different elements produce different patterns: the global alpha-element abundance5 and oxygen show a double sequence, while magnesium, silicon, and calcium present a single sequence. This could explain, at least partly, why Queiroz et al. (2020), who use a combined α-element abundance, observe a double sequence, while we see a single one with magnesium. However, this does not explain the discrepancy with Rojas-Arriagada et al. (2019), who also used magnesium. Beyond Rg = 10 kpc, the high-alpha sequence gradually vanishes. This is in agreement with the finding that the thick disc has a shorter scale length than the thin disc (Bensby et al. 2011; Cheng et al. 2012; Bovy et al. 2012). It should be emphasised that in this paragraph the term ‘sequence’ is used in the geometrical sense. It does not presuppose the number of chemical tracks that form the sequence or sequences. In particular, based on Fig. 6, it can not be excluded that the single geometrical sequence observed in the inner disc be made of two chemical tracks, with the low-alpha one restricted to a narrow metallicity range. We discuss and propose an interpretation of the inner disc sequence in Sect. 5.


The first interstellar discovery of MF took place in 1975 from the microwave emission spectrum of Sgr B2, which agrees with the rotational constants of the syn isomer (Curl 1959; Brown et al. 1975; Nummelin et al. 2000). Subsequently, it was detected in several other sources such as comet Hale–Bopp, and protostars and hot corinos (e.g., in Orion A and the protoplanetary nebula CRL 618) (Ellder et al. 1980; Ikeda et al. 2001; Cazaux et al. 2003; Bottinelli et al. 2004a, 2004b; Remijan et al. 2005, 2006; Remijan & Hollis 2006). Later, the less stable trans rotamer was detected in both the laboratory and the interstellar medium (ISM; Ruschin & Bauer 1980; Blom & Günthard 1981; Müller et al. 1983; Neill et al. 2011, 2012). The detailed mechanistic route for the interstellar synthesis of MF is a subject of considerable debate (Horn et al. 2004; Herbst 2005; Garrod & Herbst 2006; Snyder 2006; Bennett & Kaiser 2007; Occhiogrosso et al. 2011; Lawson et al. 2012). The initially assumed gas-phase model involving an ion–molecule reaction of protonated methanol (

) with formaldehyde (H2CO) fails to reproduce the large MF column density, because this pathway demands a significant activation barrier (∼128 kJ mol−1) for one of the intermediate steps (Horn et al. 2004). Instead, gas-phase radiative association between the methyl cation (

) and formic acid (HCOOH) seems energetically promising to produce H+MF and thereafter neutral MF via dissociative recombination with electrons:
1

2

However, this reaction scheme fails to reproduce the huge MF concentration observed in hot cores (Horn et al. 2004). Alternative routes suggested more recently involve ion–molecule reactions with a low barrier or no barrier between protonated methanol and formic acid (acid-catalyzed Fischer esterification) or between protonated formic acid and methanol (

 transfer):
3

4

followed by reaction (2) or exothermic proton transfer of

 (H+MF) to a base with a higher proton affinity than MF (Ehrenfreund & Charnley 2000; Neill et al. 2011, 2012).


We compute the emission line luminosity using the following equation:
1

LCO′=3.25×107SCOΔvDL2(1+z)3νobs2Kkms−1pc2,

where ν
obs is the observed CO transition frequency, D

L
 is the luminosity distance, and S
COΔv is the line-integrated flux in units of Jy km s−1. We convert the observed CO transition luminosity into CO (1–0) luminosity (L

CO(1−0)′

) by assuming that the low-J CO transitions are thermalized and optically thick, so

LCO4–3′=LCO3–2′=LCO1–0′

. Using the ratios (

rJ1=LCOJ→J−1′/LCO1–0′

) from Carilli & Walter (2013) with r
31=0.97 and r
41 = 0.87 did not significantly change our results. Furthermore, in 3C 298, we found that the molecular gas is consistent with being thermalized and optically thick (Vayner et al. 2017). These physical conditions are consistent with what is found for Mrk 231 (Feruglio et al. 2015). Finally, we convert the

LCO1–0′

 line luminosity into molecular gas mass using the CO-to-H2 conversion factor: α
CO with units of (K km s−1pc2)−1. For sources where we do not detect any narrow CO emission at the systemic redshift, we place a limit on the molecular gas mass over an aperture equal to the beam size for an emission line with a velocity FWHM of 250 km s−1. The molecular gas mass limits can be linearly scaled with a different α
CO value. For sources with detected molecular gas at the quasar’s systemic redshift, we compute the radius of the molecular gas region, which allows us to measure the gas surface density. All radii are computed using a curve-of-growth method. Effective radii refer to a region that encloses 50% of the flux, while “maximum” extent refers to a size scale that encloses 90% of the flux. In all cases, narrow emission at the systemic redshift of the quasar is spatially resolved by our observations. We deconvolve the size of the beam from all radius measurements. For sources with no detected CO emission, we use the molecular gas mass limit and the beam of the observations as a proxy for the radius. Values associated with the molecular gas at the systemic redshift are summarized in Table 3.


Two spectroscopically confirmed X-ray AGNs in the cluster core (#607, 661 in G13) are suitable candidates for ionizing the nebula. The depth of the new Chandra observation, coupled with an optimal on-axis alignment, allowed us to perform a basic X-ray spectral analysis despite the limited photon statistics (34 and 20 net counts in the observed 0.5–7 keV band for sources #607 and 661, respectively). Source #607 is characterized by a power-law spectrum with photon index Γ = 2.0 ± 0.6; the observed 2–10 keV flux is 1.7+1.1−0.6 × 10−15 erg cm−2 s−1, corresponding to a rest-frame 2–10 keV luminosity of

 erg s−1, typical of a luminous Seyfert galaxy. The X-ray spectrum of source #661, the point-like Lyα emitter (Figure 2), is flat: fitting the data with a power-law model provides

, highly indicative of strong obscuration. We then included an absorption component and fixed the photon index to 1.8, as expected for the intrinsic AGN emission (e.g., Piconcelli et al. 2005). This model results in a column density of

 cm−2, i.e., consistent with marginal Compton-thick absorption (1.5 × 1024 cm−2). The tentative detection of an iron Kα emission line at 6.4 keV (with equivalent width of ≈2.4 keV rest frame), if confirmed, would further support the heavily obscured nature of source #661. The derived 2–10 keV flux is (7.4 ± 2.2) × 10−15 erg cm−2 s−1, corresponding to a rest-frame luminosity of

 erg s−1, placing source #661 in the quasar regime. We do not detect any bright counterpart in deep Jansky Very Large Array observations at 3 GHz down to 2.7 μJy (rms), and we thus classify source #661 as radio-quiet. From aperture photometry, we estimated a Lyα flux of

 erg cm−2 s−1, corresponding to a luminosity of (1.9 ± 0.2) × 1042 erg s−1. The spectral energy distribution (SED) of #661 is shown in Figure 7. From SED modeling, which benefits from near-, mid-, and far-IR observations from Spitzer and Herschel, we estimated a bolometric luminosity for the AGN of (2.7 ± 1.5) × 1045 erg s−1. A similar value (3.2 ± 0.6 × 1045 erg s−1) is derived using the observed [O iii]λ5007 Å luminosity obtained from recent Subaru/MOIRCS spectroscopy of the galaxy (Valentino et al. 2015), converted into a bolometric luminosity as

 (Heckman et al. 2004). Assuming the luminosity-dependent bolometric correction as in Lusso et al. (2012), we predict an intrinsic 2–10 keV luminosity for source #661 of

 erg s−1. This value is consistent, within the uncertainties due to the adopted relations and measurements, with that derived from the X-ray spectral analysis reported above.


The EWs were measured with the methods described in Vásquez et al. (2015). As in Paper I, we used the sum of the EWs of the two strongest CaT lines (λ8542, λ8662) as a metallicity estimator, following the Ca II triplet method of Armandroff & Da Costa (1991). Different functions have been tested in the literature to measure the EWs of the CaT lines, depending on the metallicity regime. For metal-poor stars ([Fe/H] ≲ −0.7 dex) a Gaussian function was used with excellent results (Armandroff & Da Costa 1991), while a more general function (such as a Moffat function or the sum of a Gaussian and Lorentzian, G + L) is needed to fit the strong wings of the CaT lines observed in metal-rich stars (Rutledge et al. 1997; Cole et al. 2004). Following our previous work (Gullieuszik et al. 2009; Saviane et al. 2012) we have adopted here a G+L profile fit. To measure the EWs, each spectrum was normalised with a low-order polynomial using the IRAF continuum task, and set to the rest frame by correcting for the observed radial velocity. The two strongest CaT lines were fitted by a G+L profile using a non-linear least squares fitting routine part of the R programming language. Five clusters from the sample of Saviane et al. (2012) covering a wide metallicity range were re-reduced and analysed with our code to ensure that our EWs measurements are on the same scale as the template clusters used to define the metallicity calibration. Figure 5 shows the comparison between our EWs measurements and the line strengths measured by Saviane et al. (2012) (in both cases the sum of the two strongest lines) for the five calibration clusters. The observed scatter is consistent with the internal errors of the EW measurements, computed as in Vásquez et al. (2015). The measurements show a small deviation from the unity relation, which is more evident at low metallicity. A linear fit to this trend gives the relation: ΣEW(S12) = 0.97 ΣEW(this work) + 0.21, with an rms about the fit of 0.13 Å. This fit is shown in Fig. 5 as a dashed black line. For internal consistency, all EWs in this work have been adjusted to the measurement scale of Saviane et al. (2012) by using this relation. In Table 3 we provide the coordinates, radial velocities, and the sum of the equivalent widths for the cluster member stars, both measured (“m”) and corrected (“c”) to the system of Saviane et al. 2012.


Models of the ECSN progenitor cores suggest the onset of the electron-capture instability occurs at a unique ONeMg core mass in the mass range of 1.366–1.377 M⊙. (Miyaji et al. 1980; Nomoto 1984, 1987; Podsiadlowski et al. 2005; Takahashi et al. 2013). Electron captures cause the core to contract, and O and Ne burning is ignited in the central regions and propagates outwards in a deflagration front (Schwab et al. 2015), processing material to nuclear statistical equilibrium, where further electron captures and photdissociation accelerates the collapse (Miyaji et al. 1980; Nomoto 1987; Takahashi et al. 2013). Whether the core collapses or the deflagration disrupts the core depends sensitively on the ignition density (Isern et al. 1991; Jones et al. 2016). If the core does collapse, the explosion proceeds via delayed explosion on short timescales (Mayle & Wilson 1988; Kitaura et al. 2006; Fischer et al. 2010), and 2D simulations suggest the explosion occurs before significant convection has had time to develop (Wanajo et al. 2011) and hence a symmetric explosion results. This, coupled with the steep density gradient at the core surface, leads to very little mass loss from the core; estimates of mass loss include of order 10−3 M⊙ (Podsiadlowski et al. 2005), 10−2 M⊙ (Kitaura et al. 2006), and 1.39 × 10−2 M⊙ (1.14 × 10−2 M⊙) for the 1D (2D) models of Wanajo et al. (2009, 2011). Therefore the ONeMg progenitor core mass is a good estimate of the baryon mass MB of the resulting NS (Podsiadlowski et al. 2005). Indeed, PSR J0737-3039A and the companion to PSR J1756-2251 have gravitational masses consistent with baryon masses ∼1.37 M⊙ when their gravitational binding energies are taken into account (Lattimer & Yahil 1989). Population synthesis calculations incorporating the various binary evolution channels that might lead to production of NSs via ECSNe show that J0737-3039B most likely formed in an ECSN, and the companion to PSR J1756-2251 is consistent with such a formation scenario (Andrews et al. 2015). Other systems with candidates for ECSNe formation also exist (Keith et al. 2009; Chen et al. 2011).


While the velocity field is very regular, the velocity dispersion looks more complex. Instead of one peak extended along a southeast-northwest axis (Figs. 10, and 11), we find three (Fig. 13). The central one, elongated along the main axis of the galaxy and not perpendicular to it, is accompanied by two additional regions with wider lines to the northeast and southwest, separated by a total of 5.′′ 9 ± 0.′′2. At these locations we observe the transition between rigid body rotation and steep rotation curve to a flat one (see also Chou et al. 2007). At a radius of 2.′′45 (≈45 pc) and with a rotation velocity of 140 km s−1 we obtain with Eq. (1) of Mauersberger et al. (1996; η = 1) an enclosed mass of M2.45 = 2.1 × 108 M⊙ with an estimated error of 10%, in good agreement with Cunningham & Whiteoak (2005). For comparison, Roy et al. (2010) obtain with the H92α line 3 × 107 M⊙ for the mass inside a radius of 1″ (≈19 pc). Furthermore, we note that the nuclear region is slightly lopsided: The center of the line connecting the two outer peaks of line width is located ≈1.′′4 southwest of the continuum peak (see Table 3). Systemic velocities (Vbarycentric ≈ 571 km s−1) are found about 0.′′9 (≈15–20 pc) southwest of the continuum peak and may be closer to the position of the maser disk (Greenhill et al. 1997) given in Sect. 3.1. While uncertainties with respect to the value of the systemic velocity (Sect. 2) and the relative positions of the continuum peak and the maser disk are significant, most of the star formation represented by the continuum emission (see Bendo et al. 2016) appears to originate slightly northeast of the dynamical center. The lopsidedness of the central region of NGC 4945 also explains why molecular lines with significant absorption show integrated intensity peaks slightly shifted to the southwest: With the bulk of the continuum arising from the northeast, line emission is likely more quenched by absorption at this side of the center than in the southwestern part of the inclined nuclear disk (see Sect. 4.1.2 and Fig. 7).


Erwin (2018) showed that, in a sample drawn from the Spitzer Survey of Stellar Structure in Galaxies (S4G), the bar fraction is constant over a range of (g −r) colours and gas fractions. Their bar fraction does not increase, but rather decreases for stellar masses higher than ∼ 109.7M⊙. These results are in contrast to many SDSS-based studies cited above. Erwin (2018) argues that this apparent contradiction can be explained if SDSS-based studies miss bars in low-mass blue galaxies. In Figs 5 and 6, we showed that the newly detected bars in GZD (compared to GZ2) are weak bars in low-mass blue galaxies. Nevertheless, the ‘combined’ bar fraction in Fig. 6 is not constant over (g −r) colour and agrees well with Masters et al. (2011) for redder colours [(g −r) colour > 0.5]. Additionally, our ‘combined’ bar fraction remains roughly constant over stellar mass. As mentioned before, we conclude that strong bars drive the trends of bar fraction with (g −r) colour, stellar mass, and SFR observed in other studies (Nair & Abraham 2010b; Masters et al. 2011, 2012; Vera et al. 2016; Cervantes Sodi 2017). However, the addition of weak bars in low-mass blue galaxies is insufficient to resolve the apparent disagreement between Erwin (2018) and many SDSS-based studies (Masters et al. 2011, 2012; Vera et al. 2016; Cervantes Sodi 2017; Kruk et al. 2018), which instead seems likely to be due to the very different sample selection of the S4G and SDSS galaxy samples. For example, the median stellar mass of the sample used in Erwin (2018) is ∼109.6M⊙ (based on their Fig. 4 and the bins in the top left-hand panel of their Fig. 5). However, the median stellar mass of our sample is 1010.6M⊙. As stellar mass correlates with many parameters (including bar length), this can have major consequences. Additionally, as Erwin (2018) notes, there is also the issue of resolution to consider. With an r-band FWHM of 1.18 arcsec from DECaLS (Dey et al. 2019) and a mean redshift of 0.036, the mean linear resolution of our sample is approximately 834 pc, which is higher than the 165 pc of Erwin (2018). This explains why they observe many sub-kpc bars, while we do not. These differences in stellar mass and resolution will manifest themselves in the conclusions, so a more detailed analysis is needed for a proper comparison with Erwin (2018).