adsabs/FOCAL huggingface dataset:
The second ingredient is the probability distribution of stellar mass at given SFR and redshift:
5



dpdlogM⋆(M⋆∣ψ,z)∝M⋆M⋆M⋆,MS(ψ,z)M⋆,MSexp−logM⋆−logM⋆,MSψ,z22σlogM⋆2M⋆≥M⋆,MS(ψ,z),

where M
⋆,MS(ψ, z) is the observed redshift-dependent galaxy main sequence with log-normal scatter 



σlogM⋆≈0.2

 dex (we adopt the determination by Speagle et al. 2014 for an anlytic fit, see their Equation (28)). The main sequence is a relationship between SFR and stellar mass followed by the majority of star-forming galaxies, apart from some outliers located above the average SFR at given stellar mass (see Daddi et al. 2007; Rodighiero et al. 2011, 2015; Sargent et al. 2012; Speagle et al. 2014; Whitaker et al. 2014; Schreiber et al. 2015; Caputi et al. 2017; Bisigello et al. 2018; Boogaard et al. 2018). The expression in Equation (5) holds for an approximately constant SFR history, which is indicated both by in situ galaxy formation scenarios (see Mancuso et al. 2016b; Pantoni et al. 2019; Lapi et al. 2020) and by observations of ETG progenitors (that have on overage slowly rising star formation history with typical duration of ≲1 Gyr; see Papovich et al. 2011; Smit et al. 2012; Moustakas et al. 2013; Steinhardt et al. 2014; Cassará et al. 2016; Citro et al. 2016) and late-type galaxies (that have on the average slowly declining star formation history over a long timescale of several gigayears; e.g., see Chiappini et al. 1997; Courteau et al. 2014; Pezzulli & Fraternali 2016; Grisoni et al. 2017). In this vein, off-main-sequence objects can be simply viewed as galaxies caught in an early evolutionary stage that are still accumulating their stellar mass (which grows almost linearly with time for a constant SFR), and are thus found to be preferentially located above the main sequence or, better, to the left of it. As time goes by and the stellar mass increases, the galaxy moves toward the average main-sequence relationship, around which it will spend most of its lifetime before being quenched due to gas exhaustion or feedback processes.
Citation: Schreiber et al. 2015
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LAE galaxies are defined by a high equivalent width (EW > 20 Å) Lyα line and are believed to be composed of extremely large regions of active star formation. Many efforts have been made to detect and characterize LAE galaxies (e.g., Conselice et al. 2003; Conselice 2004; Ravindranath et al. 2006; Shimasaku et al. 2006; Bournaud et al. 2007; Ouchi et al. 2008, 2017; Elmegreen et al. 2009a, 2009b; Tacconi et al. 2010; Gronwall et al. 2011; Kashikawa et al. 2011; Mandelker et al. 2014; Moody et al. 2014; Guo et al. 2015). In general, these galaxies appear as clusters of bright clumps, sometimes with a background of continuum emission. Evidence suggests that these clumps are larger and brighter than most star-forming regions in nearby low-redshift galaxies (Elmegreen et al. 2009a). Efforts have been made in quantifying mass, star formation rates, gas composition, and kinematics, as well as other LAE properties (e.g., Nilsson et al. 2009; Ono et al. 2010a, 2010b; Swinbank et al. 2010; Tacconi et al. 2010; Shibuya et al. 2014; Livermore et al. 2015; Nakajima et al. 2016; Hashimoto et al. 2017). These have revealed a wealth of information about the early universe, but they are ultimately limited by LAE surface brightnesses. Most studies rely upon stacks of galaxies and can draw only limited inferences about individual LAEs. Other studies show that LAE dust content, particularly clumpy dust, in the interstellar medium (ISM) can have an impact on most LAE observables (Kobayashi et al. 2007, 2010; Verhamme et al. 2008; Duval et al. 2014). Finkelstein et al. (2009) showed that clumpy dust models can provide a good fit to a set of z ∼ 4.5 LAEs, although they invoked a multiphase ISM that may be unlikely to form in nature (Laursen et al. 2013). Nevertheless, dust in LAE galaxy ISM could cause some of the irregularity in LAE surface-brightness profiles (Buck et al. 2017). With limited resolution, however, it is difficult to make this distinction. A further challenge to morphological studies is that the clump sizes are near the resolution limit of instrumental point spread functions (PSFs) and often cannot be distinguished from point sources (Guo et al. 2015). As a result, direct imaging studies cannot decisively determine whether the clumps are different in nature from star-forming regions in our local universe or if the larger apparent size is merely an artifact of insufficient resolution (Shibuya et al. 2014; Kobayashi et al. 2016; Tamburello et al. 2017; Fisher et al. 2017).
Citation: Shibuya et al. 2014
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Citation: Shibuya et al. 2014
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As clusters of galaxies assemble, they dynamically transform the physical properties of in-falling cluster members. Galaxies falling into a cluster experience ram pressure from dense intracluster medium (ICM) gas that can potentially unbind their individual gas reservoirs (Gunn et al. 1972). This process referred to as ram pressure stripping (RPS) can eventually remove a galaxy’s entire gas supply, making it an important quenching pathway for satellite galaxies (Vollmer et al. 2001; Tonnesen et al. 2007). Observationally, RPS results in disturbed galaxy morphologies and trailing tails of stripped gas (e.g., Kenney et al. 2004; van Gorkom 2004; Cramer et al. 2019). The most extreme examples have been dubbed “jellyfish” galaxies, due to the evocative morphologies of their star-forming tails (Ebeling et al. 2014; Boselli et al. 2016; Poggianti et al. 2016). Prior to complete gas removal, moderate values of ram pressure have also been shown to increase the star formation rate in galaxies both observed (Crowl & Kenney 2006; Merluzzi et al. 2013; Vulcani et al. 2018) and simulated (Kronberger et al. 2008; Kapferer et al. 2009; Tonnesen & Bryan 2009; Bekki 2014). In this picture, the increased pressure initially helps compress the gas and triggers increased star formation. Other proposed processes for accretion of gas to the centers of galaxies include gravitational instabilities and the inspiral of preferentially low angular momentum clumps that lose angular momentum to a wind: drag from the nonrotating ICM operating on dense clumps (Schulz & Struck 2001; Tonnesen & Bryan 2009; Ramos-Martínez et al. 2018). Over time, the interstellar medium (ISM) is fully stripped from the galaxy and star formation ceases. Recently, a very high incidence of AGN (5/7) has been observed in a sample of jellyfish galaxies (Poggianti et al. 2017), and comprehensive follow-up of this sample has led to the identification of AGN-driven outflows (Radovich et al. 2019) and a compelling case for AGN feedback in action (George et al. 2019). It is plausible that the same mechanisms that initially promote star formation can also fuel active galactic nuclei (AGN) during the ram pressure stripping process. Indeed, outside of cluster environments, ram pressure induced shocks are known to produce nuclear inflows that can fuel AGN in the context of galaxy merger simulations (Barnes 2002; Capelo & Dotti 2017; Blumenthal & Barnes 2018).
Citation: Kapferer et al. 2009
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Theoretically predicted magnetic field in the formation of the first stars. The evolution of a dynamo in a collapsing minihalo depends on a large number of parameters: the initial density, nH,0, the turbulent velocity, vt (which we parametrize in terms of the virial velocity, vt = ϕtvvir), the temperature, T, the mass of the collapsing cloud, M0, the rate of collapse (parametrized by ϕff), and the rate at which these quantities vary with density (denoted by qx for quantity x). (The initial value of the field, B0, enters only logarithmically, and is important only if it is many orders of magnitude less than our estimate of ∼10−16 G.) Choosing values of these parameters that are consistent with simulations (e.g. those of Greif et al. 2012), we find that the time for the field to grow from its initial amplitude of ∼10−16 G to equipartition at the viscous scale, Bν ∼ 10−8 G, is less than the virial time in the minihalo; hence, the exponential growth of the field occurs at approximately constant gas density. This rapid growth of the field is consistent with that found in previous work (e.g. Schleicher et al. 2010; Schober et al. 2012b). The subsequent non-linear dynamo amplification is sufficient to bring the field energy to within about an order of magnitude of equipartition; none the less, the overall amplification of the field is generally dominated by compression. We estimate that the field first reaches equipartition with turbulent velocities of the order of 2 km s−1 (taken from simulations) at a value of ∼10−4 G; the field subsequently grows as $n_{\rm H}^{1/2}$. The field reaches equipartition with the central 5 per cent of the mass of the gas. Our conclusion that the field reaches equipartition in a minihalo at z ∼ 25 differs from that of Xu & Lazarian (2016), who found that equipartition was not reached until a time of about 6 × 108 yr (the age of the Universe at z ≃ 8) since they did not consider the increase in density that occurs in star formation.
Citation: Greif et al. 2012
Citation function: Uses
Thanks to the observations with high time resolution and high optical sensitivity from state-of-the-art facilities, various dynamic phenomena, and processes (e.g., magnetic reconnections, jet flows, and oscillatory waves) have been observed to be omnipresent in the multi-layers of solar atmosphere (Shibata et al. 2007; Tomczyk et al. 2007; He et al. 2010; Tian et al. 2014; Shen et al. 2018). In the past, magnetic reconnection and wave dissipation are viewed as two seemingly distinct mechanisms opposite to one another when dealing with the problems of coronal heating and solar wind origin (e.g., Cranmer & Van Ballegooijen 2010). Magnetic reconnection rapidly converts magnetic energy to particle energy, causing emission flare in multiple wave bands and even triggering coronal mass ejections (Priest & Schrijver 2000). Nano-jets are observed in closed loop system and have been suggested as being caused by the slingshot effect of newly reconnected magnetic field lines with a small angle of shear before reconnection (Antolin et al. 2021). Oscillatory wave signatures (e.g., oscillations in radiation intensity and Doppler velocities) have been considered as propagating or standing in both open strands or closed loops throughout the solar atmosphere (Wang et al. 2009). Flares are found to be often accompanied by quasi-periodic pulsations in multiband radiation intensities (Nakariakov et al. 2010; Van Doorsselaere et al. 2016; McLaughlin et al. 2018). The causal relation between reconnection and Alfvén waves was explored in 3D magnetohydrodynamic (MHD) simulations using the Walén test of the field-aligned current and parallel vorticity as emitting from the reconnection site (Ma et al. 1995). In the chromosphere, excitation of kink or Alfvénic waves, as well as compressive slow-mode waves, are also discovered to be associated with the formation of type-II spicules, which are launched by magnetic reconnection (He et al. 2009a, 2009b; Liu et al. 2014).
Citation: Priest & Schrijver 2000
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RX Cas is part of a group of interacting binaries showing light curves similar to β Lyrae and showing a long photometric cycle of unknown origin (Mennickent 2017). Few of these interacting binaries have been studied in terms of their physical changes during the long cycle. Here we provide a brief comparison with some of them. In RX Cas, we find that the long cycle of around 516 days can be explained in terms of changes of the physical parameters of the accretion disk, which was mentioned along with other possible causes in the past (Kříž et al. 1980). The disk turns out to be hotter and thicker at long-cycle maximum, but it does not change appreciably in radial extension. We note that a hotter disk during maximum was predicted by a simpler study in the past (Djurašević 1993). Contrarily, large changes in disk radius and thickness have been inferred in OGLE-BLG-ECL-157529, a binary characterized by orbital and long periods of 24



.

d



$ \overset{\text{d}}{.} $


8 and ∼850 days, while its long cycle can be reproduced by occultation of the gainer by a variable disk thickness (Mennickent & Djurašević 2021). On the other hand, the system OGLE-LMC-DPV-097 is characterized by a long-cycle length of ∼306 days and an amplitude of ∼0.8 mag in the I band. This binary has an orbital period of 7



.

d



$ \overset{\text{d}}{.} $


75 and relatively low stellar masses of 5.5 and 1.1 M⊙. In this system the orbital light curve changes in a systematic way during the long cycle. At the minimum of the long cycle the secondary eclipse practically disappears and during the ascending branch the system is brighter in the first quadrant than during the second quadrant. The disk radius is 7.5 R⊙ at the minimum and 15.3 R⊙ at the ascending branch of the long cycle; its temperature at the outer edge changes from 6870 to 4030 K in these two stages (Garcés et al. 2018). OGLE-LMC-DPV-065 is another interesting binary with a large amplitude of the long cycle. The long cycle is characterized by a double hump light curve in the I and V bands whose general shape is nearly constant with only minor variations. After a continuous decrease in the long-period from 350 to 218 d lasting about 13 yr, the long cycle remained almost constant for about 10 yr. However, the orbital light curve is fairly constant in shape, and no clear link between the disk structure and long cycle has been observed for this system, in a clear contrast to OGLE-LMC-DPV-097 and RX Cas (Mennickent et al. 2019). From the above we conclude that we cannot draw general conclusions about disk changes and photometric long cycles in these few systems as they show different behaviors that cannot be easily adapted to a global single picture.
Citation: Djurašević 1993
Citation function: Similarities
The size–SFR relation has been already well-defined in the local Universe investigating H ii regions in nearby spiral and irregular galaxies by Kennicutt (1988). On the other hand, outliers are mainly hosted by interacting systems as shown in the case of the Antennae galaxy (Bastian et al. 2006), and have been thoroughly investigated on a larger samples with DYNAMO (Green et al. 2014) and LARS (Messa et al. 2019). In Fig. 10, we show the relation between the size and star formation rate of clumps, including samples across different redshifts (Swinbank et al. 2009, 2012; Jones et al. 2010; Livermore et al. 2012, 2015; Johnson et al. 2017). We consider samples where SFRs was estimated through SED fitting (as for our sample) or from H α, using the prescription by Kennicutt (1988). Our sample and the Sunburst clumps (Vanzella et al. 2021a), despite being at z ∼ 2–6, have sizes comparable to local H ii regions and GCs (Swinbank et al. 2009; Jones et al. 2010; Livermore et al. 2012; Johnson et al. 2017), but they have SFRs ∼300 times higher. They also have SFRs ∼100 times higher than clumps from Wuyts et al. (2014) and Livermore et al. (2015), which are part of a sample of lensed systems at z > 2. In particular, we compare our sample with two well-known H ii regions from the local Universe: 30 Doradus and II Zw40 (Vanzi et al. 2008). One possible explanation for the high SFR measured in our sample is enhanced interactions, or larger gas reservoirs at high redshift. Similar cases have been observed in the local Universe, for example, the Antennae galaxy which is a local merging system hosting six star-forming complexes whose SFR ranges from 0.2 to 1.4 M⊙ yr−1, significantly higher than other local star-forming regions. Three of those complexes show signatures of Wolf–Rayet stars, implying young ages of $\sim 5\, \rm Myr$ (Bastian et al. 2006). Similar properties are observed in the Sunburst 5.1 knot presented in detail in Vanzella et al. (2021a) and in some of our clumps shown in fig. D.1 of Vanzella et al. (2021b). Other scenarios, such as the fragmentation of gas-rich discs, are potentially responsible for the observed high SFR among high redshift clumpy structures (Noguchi 1999; Dekel et al. 2009). During this process, cold gas cools becoming unstable and the galactic disc starts to fragments and forms clumpy structures. Further on, such newly formed structures lead to the increased SFR as discussed in i.e. Immeli et al. (2004).
Citation: Johnson et al. 2017; Johnson et al. 2017
Citation function: Uses; Similarities; Similarities; Differences
The reasons for such a tight – and linear – correlation between \hbox{$L^{\prime}_{\rm CO(1{-}0)}$}LCO(1−0)′ and M∗ (or LK) observed consistently across different galaxy samples except in early type galaxies have been little explored in the literature. The interpretation favoured by Leroy et al. (2005) is that CO emission and stars are linked through the hydrostatic pressure in the disk, which depends mainly on the stellar surface density (Σ∗) and sets the rate at which Hi is converted into H2. An alternative explanation that we propose here goes back to the nature of the optically thick 12CO emission and to the approximately linear relation between 12CO luminosity and virial mass found for GMCs (see e.g. Scoville et al. (1987), Solomon et al. (1987), and Bolatto et al. (2013) for a more recent compilation). By extrapolating from the relation shown by Scoville et al. (1987), our observed \hbox{$L^{\prime}_{\rm CO(1{-}0)}-M_*$}LCO(1−0)′−M∗ correlation may be so tight and close to linear simply because the global 12CO luminosity is a very good tracer of the dynamical mass in star-forming galaxies, assuming that in this class of objects the bulk of the CO emission traces molecular gas clouds in virial motions (for example in a rotating disk). This explanation of course assumes that in most star-forming galaxies the stellar mass is also a good tracer of the dynamical mass. Following on from our hypothesis, a possible explanation for the break down of the \hbox{$L^{\prime}_{\rm CO(1{-}0)}-M_*$}LCO(1−0)′−M∗ relation in early types is that in these sources, even when CO is detected, the motions of the CO-emitting clouds are poor tracers of the total dynamical mass of the system. Interferometric CO observations of large samples of early type galaxies have indeed shown that the CO emission in these objects tends to be rather compact (on average extending over ~ 1 kpc) compared to the optical extent of the galaxy (Alatalo et al. 2013; Davis et al. 2013).
Citation: Bolatto et al. (2013)
Citation function: Uses; Uses
Combining these different velocity signatures, the data show strong signatures of two gas components at different velocities (around 6 km s−1 apart) that converge to a common intermediate velocity at the location of the infrared dark cloud and active star-formingregion, similar to filament formation via gravitationally driven, converging gas flows (e.g., Gómez & Vázquez-Semadeni 2014). We interpret these signatures as indicators of converging gas streams that may trigger the star formation event at its center (e.g., Vázquez-Semadeni et al. 2006; Heitsch et al. 2008; Banerjee et al. 2009; Gómez & Vázquez-Semadeni 2014). Position-velocity diagrams based on simulations of cloud-cloud collisions sometimes show a characteristic pattern of lower-level emission between two main velocity components, a so-called bridging feature (e.g., Haworth et al. 2015, 2018). Similar signatures were also reported in observations (e.g., Jiménez-Serra et al. 2010; Henshaw et al. 2013; Dobashi et al. 2019; Fujita et al. 2019). The pv-diagrams of the G28.3 region presented here (Fig. 9) show different signatures in the sense that there is not a lower-intensity bridge between the two well-defined components, but that the two velocity components converge at the center of the cloud toward a central, high-intensity velocity component. However, the absences of a bridging feature does not necessarily rule out the formation of G28.3 in a cloud-cloud collision, as this feature is not always visible in simulations. For example, simulations by Bisbas et al. (2017) show that the low-intensity bridge feature may merge into a centrally peaked pv-diagram during the evolution of the cloud-cloud collisions, while the colliding-cloud simulations of Clark et al. (2019) yield only a single central velocity component in CO or [CI], with multiple components only becoming apparent when the [CII] emission from the cloud is considered. In addition, the multiple components and bridging feature may not be visible in cases where our line of sight is oriented at a large angle to the direction of motion of the clouds. We return to the interpretation in Sects. 4.4 and 4.5.
Citation: Jiménez-Serra et al. 2010
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We also present the correlation functions of centrals and satellites in Fig. 17. This is a worthwhile exercise for a variety of reasons, not least that there is information on the small-scale IA signal missed in the large-scale fits. While we cannot, at the present time, fit the IA signal on scales ∼1 h−1 Mpc, the qualitative comparison can be instructive. Given that there is some evidence that they behave differently, we consider blue and red satellites/centrals separately here. We also drop the full sample density tracer, and instead use one of the four (red/blue, satellite/central) subsamples. The motivation here is that, while on large scales, the density tracer is effectively just that: a probe of the large-scale matter distribution multiplied by a linear galaxy bias, on scales approaching the one halo regime this no longer holds. Fig. 17 shows these new data vectors. As shown we measure both wg + and w++, and recompute the covariance matrices with the appropriate densities. For reference, the dark red crosses also show the equivalent satellite/central red galaxy wg + correlations from KiDS×GAMA here (c.f. Johnston et al. 2019 fig. 7, red points/band). On large scales at least, our illustristng red sample is consistent with their measurements. There are a few interesting features here to note, however. First, we see a relatively strong red galaxy 1h contribution on scales 1 h−1 Mpc. Although the general trends match the real data, with ss and to a lesser extent cs exhibiting strong scale-dependent IAs in this regime, the magnitude is somewhat higher in our sample. This is particularly interesting, given that our sample characteristics are similar (〈L〉/L0 = 0.91 and 0.34 for our red and blue samples, respectively, compared with their ∼0.99 and 0.50). As discussed briefly in Section 6.2.1, we observe a persistent non-zero IA signal in blue galaxies on large scales; here we can see it is dominated by the cc correlation, with a smaller contribution from sc. Also notable is that, in line with some previous direct IA measurements (e.g. Singh et al. 2015), the large-scale satellite correlations do not appear to vanish on large scales. Focusing on the right-hand panels, the purple and pink points are consistently positive and non-zero. While small compared with the red central terms, and consistent with the dark red points from GAMA, there appears to be a detectable signal at the precision allowed by illustristng.
Citation: Johnston et al. 2019
Citation function: Uses; Uses; Similarities; Compare/Contrast; Similarities; Differences; Compare/Contrast