adsabs/FOCAL huggingface dataset:
The coronal rain plasma can be distinguished from the prominence material by looking at their trajectories and average speeds. The timescale on which the coronal rain forms following the heating onset is much shorter than for the quiescent scenario; in the studied event condensations appear 10 min after the reconnection event, whereas observations of quiescent rain suggests it recurs in the same loop of the order of hours (Antolin & Rouppe van der Voort 2012; Kohutova & Verwichte 2016). The period of the loop heating-condensation cycle in the quiescent scenario is equivalent to the time it takes for the sustained footpoint heating to refill the loop sufficiently with evaporated plasma to reach the thermally unstable regime, after the loop has been evacuated by the previous coronal rain event. This short timescale for coronal rain formation is likely a consequence of the heating input being much greater than in the quiescent case and of the short length of the studied loop. The 1D numerical simulations suggest that although the loop length is a contributing factor, the heating input is the main factor affecting the coronal rain formation timescale (Froment et al. 2018). The thermal instability in the case associated with magnetic reconnection is also more concentrated spatially and only a certain fraction of the loop with a cross section of around 5 Mm width becomes unstable. This implies that the heating that triggers the thermal instability is more localised and only affects a small number of field lines in the loop. As the thermal conduction acts predominantly along the magnetic field, most of the matter and energy transport occurs along the affected field lines. Comparing this to the quiescent scenario, the typical width of the loop bundles observed to undergo condensation formation is around 10−15 Mm (Antolin & Rouppe van der Voort 2012; Kohutova & Verwichte 2016), and in some cases, reaching 40 Mm (Auchère et al. 2018; Froment et al. 2019).
Citation: Kohutova & Verwichte 2016
Citation function: Compare/Contrast
Understanding the redshift dependence of galaxy evolution requires disentangling the evolution of an individual galaxy within a population from the evolution in the average properties of the population as a whole (due to the continual addition of newly formed galaxies with different properties to the extant population). A key method of studying how both individual and populations of galaxies evolve is by analysing their growth in mass and size. An individual star-forming galaxy is expected to evolve along the relation of star-forming galaxies in the mass–size plane (Lilly et al. 1998; Ravindranath et al. 2004; Trujillo et al. 2006; Pezzulli et al. 2015; van Dokkum et al. 2015), while the average size of the population as a whole increases with decreasing redshift due to processes such as mergers (Hopkins et al. 2009; Naab, Johansson & Ostriker 2009) and ’puffing up’ due to strong active galactic nuclei (AGNs) feedback (Fan et al. 2008, 2010), but also new galaxies forming with larger radii (van Dokkum & Franx 2001; Carollo et al. 2013). In other words, star-forming galaxies at both low and intermediate redshifts follow parallel tracks in the mass–size plane (Speagle et al. 2014; van Dokkum et al. 2015), with their starting location in the plane depending on redshift. Therefore, when analysing star-forming galaxies in the mass–size plane at $z$ ∼ 0, we are observing the combined effect of both the evolution of individual galaxies throughout their lifetimes and the evolution of the population due to the addition of new members and loss of old members (as they quench). We want to disentangle these two effects to understand how the redshift range over which a galaxy formed and evolved influences its evolutionary path. We therefore need to understand how the processes influencing a galaxy may be regulated by the broader conditions of the Universe and how these conditions change with redshift. An important tool to measure the impact of various processes is the analysis of scaling relations, which quantify the link between different galaxy parameters to determine their dependence. Specifically, scaling relations between stellar population parameters and galaxy structure allow us to quantify how processes involved in star formation and stellar mass assembly interrelate with processes dominating structural and dynamical changes.
Citation: van Dokkum & Franx 2001
Citation function: Background; Background; Background; Background
One explanation for the low effective temperatures is that the TDE thermal emission does not originate from the accretion disc, but from an outflow supported by radiation pressure from the disc’s super-Eddington accretion rate (e.g. Loeb & Ulmer 1997; Strubbe & Quataert 2009; Lodato & Rossi 2011; Metzger & Stone 2016; Roth et al. 2016; Curd & Narayan 2019). The TDE’s high photosphere radius and low temperature can then be explained by the increased emitting area from the optically thick, expanding outflow, launched from the accretion disc or SMBH. These outflows can explain the nearly constant temperatures inferred from the spectrum of optically bright TDEs (Strubbe & Quataert 2009; Miller 2015), and may lead to observable emission or absorption line features in the TDE’s spectrum (Strubbe & Quataert 2011; Roth et al. 2016; Roth & Kasen 2018). Hydrodynamical simulations show that the outflow can be supported not only by radiation pressure from the compact disc (Dai et al. 2018; Curd & Narayan 2019) but also by shocks driven by stream–stream collisions during the circularization of stellar debris (Liptai et al. 2019; Lu & Bonnerot 2020). However, to power the outflow, a significant fraction of the tidally disrupted star’s rest-mass energy must be liberated ($0.05 \, {\rm M}_\odot \, c^2 \sim 10^{53} \, {\rm erg}$), much larger than the typical energy liberated by an optically bright TDE’s early emission ($\sim \! 10^{49}{\!-\!}10^{51} \, {\rm erg}$; e.g. Komossa 2015; van Velzen et al. 2020). This so-called missing energy problem has a number of proposed solutions. For instance, some argue most of the rest-mass energy is radiated in the unobservable far-UV wavelength bands (e.g. Lu & Kumar 2018; Jonker et al. 2020), while others propose this energy is carried away by a jet whose emission is unobservable for most TDE viewing angles (Dai et al. 2018). Some authors suggest this energy is never emitted in the first place, but rather becomes trapped due to the TDE disc and outflow’s high optical depth (photon trapping; e.g. Curd & Narayan 2019). The wind model has yet to conclusively address the missing energy problem.
Citation: Miller 2015
Citation function: Similarities
An extreme example of a rapid decay is V404 Cyg. The parameters of the long Porb system were chosen to be close to those of V404 Cyg, whose last outburst lasted only a couple of weeks and showed a pronounced disc outflow, conjectured to be a thermal wind (Muñoz-Darias et al. 2016). The mass in the disc at the onset of the outburst is ∼3 × 10−7 M⊙ in our models1. Blowing away most of this mass would require a sustained outflow rate of ≈ 10−5 M⊙ yr−1 ≈ 30 ṀEdd over 15 days for a 9 M⊙ black hole. Such very high mass outflow rates may be reached close to the Eddington luminosity as electron scattering contributes to the driving force of the wind. Figure 7 shows the mass outflow rate in the wind diverges near L ≈ 0.7 LEdd due to the estimated radiation driving correction (Eq. (4)). In principle, it might thus be possible to shorten the outburst of V404 Cyg to a couple of weeks by fine-tuning the model parameters to sample this high luminosity region. In support, observations of V404 Cyg do indicate the source likely reached LEdd (Kimura et al. 2016) and was enshrouded by rapidly varying Compton-thick outflowing material (Sánchez-Fernández et al. 2017) with an estimated Mw ≈ 4 × 10−6 M⊙ lost to the wind (Casares et al. 2019). The lower effective gravity due to the high radiation should enhance the wind (Proga & Kallman 2002) but the mass loss must saturate at some level as the outflow becomes optically thick. Higginbottom et al. (2019) do not find a significantly enhanced Ṁw near LEdd in their radiation-hydrodynamic simulations of thermal winds, but these neglect radiative driving by electron scattering. If winds are boosted near Eddington, a puzzle is why GRS 1915+105 has not been affected as much as V404 Cyg despite its luminosity also being close to Eddington and its disc size even greater. If the short duration of the V404 Cyg was due to a thermal wind, then this wind likely required very specific conditions. Instead, we speculate that the angular momentum transport was instead dominated by the jet. The system likely stayed in the (very) bright hard state during the outburst, where it has a strong jet which is almost certainly coupled to the accretion flow via the magnetic fields and could be responsible for angular momentum transport through the hot flow in this state (e.g. Ferreira et al. 2006).
Citation: Sánchez-Fernández et al. 2017
Citation function: Similarities; Similarities
Photometric Calibration: Photometric calibration was performed using the stellar locus regression technique (SLR: Ivezić et al. 2004; MacDonald et al. 2004; High et al. 2009; Gilbank et al. 2011; Coupon et al. 2012; Desai et al. 2012; Kelly et al. 2014). Our reference stellar locus was empirically derived from the globally calibrated DES Y1A1 stellar objects in the region of the Y1A1 footprint with the smallest E(B − V) value from the Schlegel et al. (SFD; 1998) interstellar extinction map. We performed a 1″ match on all Y1 and Y2 objects with S/N > 10 observed in r-band and at least one other band. We then applied a high-purity stellar selection based on the weighted average of the spread_model quantity for the matched objects (





; see below). The average zero point measured in Y1A1, ZPgrizY = {30.0, 30.3, 30.2, 29.9, 28.0}, was assigned to each star as an initial estimate. Starting from this coarse calibration, we began an iterative procedure to fix the color uniformity across the survey footprint. We segmented the sky into equal-area pixels using the HEALPix scheme (Górski et al. 2005). For each ∼0.2 deg2 (resolution nside = 128) HEALPix pixel, we chose the DES exposure in each band with the largest coverage and ran a modified version of the Big MACS SLR code (Kelly et al. 2014)41

41

https://code.google.com/p/big-macs-calibrate/

 to calibrate each star from the reference exposure with respect to the empirical stellar locus. These stars became our initial calibration standards. We then adjusted the zero points of other CCDs so that the magnitudes of the matched detections agreed with the calibration set from the reference exposure. CCDs with fewer than 10 matched stars or with a large dispersion in the magnitude offsets of matched stars (σZP > 0.1 





) were flagged. For each calibration star, we computed the weighted-average magnitude in each band using these new CCD zero points; this weighted-average magnitude was used as the calibration standard for the next iteration of the SLR. In the first iteration, we assigned SLR zero points to the calibration stars based on the HEALPix pixel within which they reside. In subsequent iterations, we assigned SLR zero points to the calibration stars based on a bi-linear interpolation of their positions onto the HEALPix grid of SLR zero points. After the second iteration, the color zero points were stable at the 1–2 





 level. The absolute calibration was set against the 2MASS J-band magnitude of matched stars (making use of the stellar locus in color-space), which were de-reddened using the SFD map with a reddening law of AJ = 0.709 × E(B − V)SFD from Schlafly & Finkbeiner (2011). The resulting calibrated DES magnitudes are thus already corrected for Galactic reddening by the SLR calibration.
Citation: Górski et al. 2005
Citation function: Uses
Within the framework of the fireball shock model, Pe'er etÂ al. (2007) proposed a method to infer central engine parameters using observed data. With the measured temperature and flux of an identified thermal component in the spectrum, along with a flux ratio between thermal and non-thermal components, one may infer the size of the jet at the base of the outflow, r0, and the dimensionless entropy of the outflow,  (which is also the bulk Lorentz factor of the outflow, if the photosphere radius is greater than the fireball coasting radius). Some authors have applied this method to some Fermi GRBs (Iyyani etÂ al. 2013; Preece etÂ al. 2014; Ghirlanda etÂ al. 2013). The derived central engine parameters are sometimes ad hoc or inconsistent. For instance, the analyses for GRB 110721A (Iyyani etÂ al. 2013) and for GRB 130427A (Preece etÂ al. 2014) led to a curious conclusion that the bulk Lorentz factor of the outflow of different layers are decreasing with time. This would lead to no, or at most very inefficient, internal shock emission. Yet both bursts have dominant non-thermal emission. More curiously, the data of GRB 110721A (Iyyani etÂ al. 2013) require that r0 is rapidly varying with time by 2â3 orders of magnitudes. This is hard to imagine given the well-believed paradigm of the GRB central engine: If the engine is naked, the size of the engine (a hyper-accreting black hole or a millisecond magnetar) is around r0 â¼ 107âcm; if an extended envelope of a collapsar progenitor is considered, the fireball may be âre-born,â with r0 â¼ R*Î¸j â¼ 109R*, 10Î¸j, â1âcm (where R* is the size of the progenitor star, and Î¸j is the jet opening angle). If one considers the depletion of the envelope, r0 should decrease with time. However, Iyyani etÂ al. (2013) showed that r0 increases from 106âcm to 108âcm early on, and then decreases mildly after 2Â s. These absurd conclusions suggest that the starting point of the analysis, i.e., the assumption of a pure fireball model, might not be valid. It is interesting to see whether a hybrid ejecta photosphere model may solve the problem. Incidentally, Ghirlanda etÂ al. (2013) analyzed another burst, GRB 100507, using the fireball framework (Pe'er etÂ al. 2007), but found that the derived r0 remains constant and reasonable. The jet composition of that burst may be more close to a fireball. It would be interesting to see whether a general theoretical framework can be established, which may be reduced to the standard fireball framework when Ï0  1.
Citation: Preece etÂ al. 2014
Citation function: Compare/Contrast; Compare/Contrast; Compare/Contrast
In Figure 1, we show the archival 850 μm polarization data from the legacy program of SCUPOL with the JCMT (Matthews et al. 2009) (effective 20″ beam ∼0.76 pc). Matthews et al. (2009) collected the JCMT data toward the GC, where the B-field is sampled on a 10″ grid. The linearly polarized light from dust grains is frequently used to probe the integrated plane-of-sky B-field morphology. Interstellar dust grains are elongated, with their minor axes parallel to the B-field. The thermal emission from the aligned dust grains is then polarized with polarization segments perpendicular to the field lines (Cudlip et al. 1982; Hildebrand et al. 1984; Hildebrand 1988; Lazarian 2000; Andersson et al. 2015). The dust polarization can, therefore, reveal the plane-of-sky projected B-field orientations. In Figure 1, the SCUPOL B-field segments are overlaid on the JCMT 850 μm map (Di Francesco et al. 2008). The segments are plotted with p/dp ≥ 2, 3, 4. The 450 μm Submillimeter Polarimeter for Antarctic Remote Observing (SPARO) B-field (Novak et al. 2003) is also overlaid with a resolution of 6′ (corresponding to a linear scale of 13.7 pc). The low-resolution SPARO map traces the large-scale B-field which is parallel to the plane of our Galaxy. This alignment of field orientations with the Galactic plane is attributed to a large-scale toroidal B-field configuration (i.e., azimuthal field). The SCUPOL 850 μm B-field as well as the continuum are clearly detected and resolved along and across the sub-features in the GC at a 20″ resolution (=0.76 pc). The detected B-field orientations (ΦB) vary enormously over the entire map, ranging from −90° to 90° (0° is west, positive is counterclockwise). Nevertheless, the ΦB varies smoothly and systematically along certain substructures, revealing organized patches in, e.g., the CND, the giant molecular cloud 20/50 MC (e.g., Güsten et al. 1981), and the HVCC CO 0.02–0.02 (Oka et al. 1999). The azimuthal correlation seen in the CND is providing the link between the model proposed by Wardle & Königl (1990) and the SCUPOL 850 μm polarization data. We are exploring this link in the following sections. A comparison of polarization data is presented in Appendix A. We find that the morphology of the B-field is consistent with different threshold cuts in p/dp. Hence, in order to work with the maximum of independent data points, we present data with p/dp ≥ 2, and we will focus on the B-field structure of the CND.
Citation: Cudlip et al. 1982
Citation function: Uses
Mainly four mechanisms are considered to be causing the observed solar radio wave emissions: two causing incoherent and two causing coherent emissions. Incoherent emissions can be due to bremsstrahlung and gyrosynchrotron radiation. In them, every electron radiates independent on the others. The total emission is simply the sum of the emissions of every single electron (Rybicki & Lightman 1979; Dulk 1985; Melrose 2017; Nindos 2020). Comparing with coherent emission mechanisms, the incoherent emission mechanisms are better understood. The two coherent emission mechanisms are plasma (Ginzburg & Zhelezniakov 1958; Melrose 1970a, 1970b; Zheleznyakov & Zaitsev 1970a, 1970b) and electron cyclotron maser (ECM) emissions (Twiss 1958; Gaponov 1959; Schneider 1959; Pritchett 1984a). In contrast to the incoherent emission mechanisms, coherent emission can explain (a) the high brightness temperatures, (b) the short eruption timescales, (c) the narrow frequency bands, and (d) the strong polarization of Type I, II, III solar radio bursts (SRBs) and solar radio spikes (Aschwanden 2005). And coherent radio emission mechanisms involve plasma instabilities. The plasma emission follows a beam or bump-on-tail instability, the source of free energy of which is related to an electron velocity distribution 






u


∥


·
∂


f




u


∥





/

∂


u


∥


>
0


. On the other hand, the ECM instability requires a positive gradient in the electron velocity distribution along the direction perpendicular to the ambient magnetic field (




∂


f




u


⊥





/

∂


u


⊥


>
0


) (see Melrose 1986, 2017). Here, f is the electron distribution function and u∥, u⊥ are the electron velocities parallel and perpendicular to the ambient magnetic field, respectively. The expressions 






u


∥


·
∂


f




u


∥





/

∂


u


∥


>
0


 and 




∂


f




u


⊥





/

∂


u


⊥


>
0


 indicate an electron beam and a ring (or a loss cone or a horseshoe) distribution in the direction parallel and perpendicular to the ambient magnetic field, respectively. The existence of electron velocity distributions 






u


∥


·
∂


f




u


∥





/

∂


u


∥


>
0


 has been conjectured based on observations of, e.g., SRBs and hard X-ray bursts as well as confirmed by observations of solar energetic particles (Chen et al. 2015, 2018; Cairns et al. 2018). Numerical simulations have also considered possible formation mechanisms of velocity distributions with 




∂


f




u


⊥





/

∂


u


⊥


>
0


, e.g., by magnetic reconnection (e.g., Büchner & Kuska 1996; Bessho et al. 2014; Shuster et al. 2014; Zhou et al. 2016; Treumann & Baumjohann 2017; Muñoz & Büchner 2018a; Voitcu & Echim 2018; Yao et al. 2021a). It is, however, still under debate which coherent mechanism dominates and which role different features of the electron distribution play for the emission of coherent radio waves from the solar corona.
Citation: Schneider 1959
Citation function: Background; Background
Various methods have been developed in the past to derive stellar atmospheric parameters from large number of medium-to-low resolution spectra (Recio-Blanco, Bijaoui & de Laverny 2006; Lee et al. 2008a, Wu et al. 2011). The approaches generally fall into two main categories of method (Wu et al. 2011): the minimum distance method (MDM) and non-linear regression method. Both categories of method have been applied to large stellar spectroscopic surveys, including the SEGUE (Yanny et al. 2009), RAVE (Steinmetz et al. 2006), APOGEE (Majewski et al. 2010) and LAMOST (Zhao et al. 2012). The MDM is usually based on spectral template matching, and searches for the template spectrum that has the shortest distance in parameter space from the target spectrum. The χ2 minimization, cross-correlation, weighted mean algorithm and the k-nearest neighbour are thought to be specific cases of MDM (Wu et al. 2011). Software and pipelines developed based on those algorithms include the tgmet (Katz et al. 1998), matisse (Recio-Blanco et al. 2006), sspp (Lee et al. 2008a), ulyss (Koleva et al. 2009), that of Allende Prieto et al. (2006) and of Zwitter et al. (2008). The non-linear regression method is sometimes also referred to as the artificial neural network. The method constructs a functional mapping between the spectra and stellar atmospheric parameters by training a library of template spectra with non-linear algorithms such as the principal component analysis, and then apply the mapping to target spectra. Related work can be found in Re Fiorentin et al. (2007) and Lee et al. (2008a). In addition to the above two categories of method, other approaches have been developed, for example, the line-index method based on the relations between the stellar atmospheric parameters and the equivalent widths (EWs) of spectral features and/or photometric colours (Beers et al. 1999; Wilhelm, Beers & Gray 1999; Cenarro et al. 2002). More recently, a Bayesian approach to determine stellar atmospheric parameters combing spectral and photometric measurements has been developed by Schönrich & Bergemann (2014).
Citation: Steinmetz et al. 2006
Citation function: Background; Background