adsabs/FOCAL huggingface dataset:
A different approach is to count the amount of baryonic material within the proto-cluster bounds associated with the member galaxies and attempt to relate that back to the overall mass of the structure, an approach which has been employed successfully at low redshift when the galactic baryonic content of cluster member galaxies is dominated by stars (Andreon 2012). The approach we take here is similar to that of Lemaux et al. (2014a). Briefly, the total amount of stellar matter of the zspec members is counted and a completeness correction is made to this value for galaxies at stellar masses 

$\log(\mathcal{M}_{\ast}/M_{\odot})\ge 9.5$log(M∗/M⊙)≥9.5
 (see Sect. 4.2.1 for the reasoning behind this cut) based on the number of zphot members and non-members without secure spectral redshifts within the bounds of the proto-cluster and the likelihood of their being true members. An additional correction is made to correct for galaxies in the stellar mass range 

$8.0 < \log(\mathcal{M}_{\ast}/M_{\odot})< 9.5$8.0log(M∗/M⊙)9.5
 by integrating the stellar mass function of Davidzon et al. (2017) appropriate for this redshift. Here we additionally make the assumption that stellar mass comprises 50% of the baryonic content of galaxies by mass at these redshifts, a value broadly consistent with the few measurements made at or near these redshifts (e.g., Tacconi et al. 2010; Capak et al. 2011b; Schinnerer et al. 2016; Scoville et al. 2016). We assume for the purposes of this calculation that the proto-cluster is a closed system, with all gas being converted to stars by z = 0 and that the completeness-corrected galaxy population which lies within Rproj ≤ 3 Mpc at z ~ 4.57 comprises the entirety of the galaxy population which will eventually be contained within the cluster virial radius at z = 0. The latter assumption is broadly consistent with simulations (Muldrew et al. 2015) to ~10% accuracy, a factor which we account for in the calculation below. Any total to stellar mass conversion then provides a z = 0 total mass, which we de-evolve to z ~ 4.57 using the correction factors of Muldrew et al. (2015) appropriate for z ~ 4.57 (0.20 ± 0.03), a correctionwhich is appropriate for descendents of all masses. Into this formalism we input the resulting completeness-corrected baryonic content of 

$\log(\mathrm{\Sigma}\mathcal{M}_{\ast}/M_{\odot})=12.39^{0.05}_{-0.07}$log(ΣM∗/M⊙)=12.39−0.07+0.05
 to the r200 stellar mass to M500 total mass relation of Andreon (2012) and scale the resulting M500 to the virial radius (Rvir = 0.33 Mpc) using the methods presented in Lemaux et al. (2014a) giving:

(2)

\begin{equation*}
\log(\mathcal{M}/\mathcal{M}_{\odot})_{z=4.57, \, \mathrm{\Sigma}\mathcal{M}_{\ast, corr}} = 13.31^{0.23}_{-0.27}.\end{equation*}log(M/M⊙)z=4.57, ΣM∗,corr=13.31−0.27+0.23.
Citation: Tacconi et al. 2010
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Recently, rotationally supported disks have been found not only in Class I sources but also in some Class 0 sources (e.g., Murillo et al. 2013; Yen et al. 2013, 2017; Ohashi et al. 2014; Tobin et al. 2015, 2016b, 2016a; Seifried et al. 2016; Aso et al. 2017; Lee et al. 2017; Okoda et al. 2018). In spite of these extensive studies, there is still controversy regarding when and how a disk structure is formed around a newly born protostar. Moreover, the disk formation process has been revealed to be much more complicated for binary and multiple cases, both in observations (Dutrey et al. 2014; Tokuda et al. 2014; Takakuwa et al. 2014, 2017; Tobin et al. 2016b, 2016a; Boehler et al. 2017; Artur de la Villarmois et al. 2018; Alves et al. 2019) and in numerical simulations (e.g., Bate & Bonnell 1997; Kratter et al. 2008; Fateeva et al. 2011; Shi et al. 2012; Ragusa et al. 2017; Satsuka et al. 2017; Price et al. 2018; Matsumoto et al. 2019). For instance, circumbinary/circummultiple disk structures with a spiral structure as well as a circumstellar disk for each component are reported (e.g., Tobin et al. 2016a; Takakuwa et al. 2017; Artur de la Villarmois et al. 2018; Matsumoto et al. 2019; Alves et al. 2019). In addition, it is not clear how molecules are processed during the disk formation process and what kinds of molecules are finally inherited by protoplanetary disks and potentially by planets. Understanding these processes is crucial, as they will provide important constraints on the initial physical and chemical conditions for the planetary system formation study. In this context, physical and chemical structures and their mutual relation for disk-forming regions of low-mass protostellar sources have been investigated with the Atacama Large Millimeter/submillimeter Array (ALMA; e.g., Sakai et al. 2014b, 2014a; Oya et al. 2016, 2017, 2018, 2019; Imai et al. 2016, 2019; Jacobsen et al. 2019). These studies reveal that infalling envelopes and rotationally supported disks are not smoothly connected to each other either in physical structure or in chemical composition, unlike previous expectations.
Citation: Price et al. 2018
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Citation: Takakuwa et al. 2014
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NGC 4418 is one of the closest and best-studied galaxies hosting CONs. It is an LIRG with its 8–1000 μm infrared luminosity (LIR) of 1.4 × 1011 L (Sanders et al. 2003)4

4
With our assumed distance, the luminosity is larger than the threshold for the LIRG classification of LIR = 1 × 1011 L (Sanders & Mirabel 1996). We note, however, that NGC 4418 is sometimes classified as a non-LIRG if one adopts smaller distance to the galaxy. For example, with its recession velocity (cz) and an assumed Hubble constant of H0 = 75 km s−1 Mpc−1, the distance is estimated to be 28.32 Mpc and LIR = 9. 4 × 1010 L.
 at a distance of 34 Mpc (1″ = 165 pc; Sakamoto et al. 2013).5

5
Sakamoto et al. (2013) adopted the distance from Sanders et al. (2003), in which distance is calculated with cz using the cosmic attractor model outlined in Appendix A of Mould et al. 2000, using H0 = 75 km s−1 Mpc−1 and adopting a flat cosmology in which ΩM = 0.3 and Ωλ = 0.7. We adopt this distance for consistency with recent closely related studies of this galaxy (Aalto et al. 2012; Costagliola et al. 2013; Varenius et al. 2014).
 In spite of its huge infrared luminosity, the host galaxy is classified from the optical morphology as an ordinary Sa-type galaxy ([R’]SAB[s]a; de Vaucouleurs et al. 1991) with moderate inclination (i = 62°; taken from Sakamoto et al. 2013 for consistency; based on Jarrett et al. 2000). It has a neighbor galaxy about 32 kpc (32) away (VV 655 = MCG+00- 32-013 = SDSS J122704.47-005420.6; 





 km s−1; Evans et al. 2003; Varenius et al. 2017). Varenius et al. (2017) found a bridge of H i 21 cm emission connecting these two galaxies and argued that their strong tidal interaction occurred about 190 Myr ago. The optical nuclear emission line spectrum of NGC 4418 is classified as LINER (Armus et al. 1989; Shi & Gu 2005) or Seyfert 2 (Baan et al. 1998), but no direct signature of AGNs such as very broad permitted lines is known (see also Lehnert & Heckman 1995). NGC 4418 is deficient of X-ray emission for its infrared luminosity, and its interpretation has been a matter of controversy. Maiolino et al. (2003) tentatively identified, with low statistics, a Compton-thick (reflection-dominated) AGN on the basis of its small photon index (or flat X-ray spectrum; 





, where 





, E is the photon energy, and N(E) is the photon number density). On the other hand, Lehmer et al. (2010) found the opposite (soft) spectral index (





), which is even softer than the one of Compton-thin AGNs (Γ ≃ 1.7; Mushotzky et al. 1993). We note that both analyses are based on the Chandra AICS-S data, and Lehmer et al. (2010) added ≃30% more integration onto the earlier data used by Maiolino et al. (2003).
Citation: de Vaucouleurs et al. 1991
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There is a similarity between the ISM and the young stellar structures identified here. First, similar to the young stellar structures, the ISM also displays irregular morphologies and contains large amounts of substructures (clouds, clumps, cores, filaments, etc.) that are hierarchically organized (Rosolowsky et al. 2008). Second, the ISM substructures also follow a power-law size distribution, which indicates a scale-free behavior (e.g., Elmegreen & Falgarone 1996). The third aspect of their similarity comes from the fractal dimension. The projected fractal dimension of the ISM has been investigated based on the perimeter–area relation of its projected boundaries. Typical values are close to D2 = 1.4–1.5 (e.g., Beech 1987; Scalo 1990; Falgarone et al. 1991; Vogelaar & Wakker 1994; Lee 2004; Lee et al. 2016; although smaller values have also been reported by, e.g., Dickman et al. 1990; Hetem & Lepine 1993), which are consistent with the fractal dimension as derived for the young stellar structures. Using power-spectrum analysis, Stanimirovic et al. (1999, 2000) reported D2 = 1.4 or 1.5 for the ISM in the SMC, also close to that of the young stellar structures. On the other hand, it is possible to measure the volume fractal dimension of the ISM, since clouds along the line of sight can be distinguished by their velocities. For instance, Elmegreen & Falgarone (1996) reported D3 = 2.3 ± 0.3 based on the size distribution for a number of Galactic molecular clouds, and Roman-Duval et al. (2010) found D3 = 2.36 ± 0.04 using the mass–size relation. If the relation D3 = D2 + 1 holds for the ISM, these results would not be far from the fractal dimension of the young stellar structures. Unfortunately, there is no reported measurement of the fractal dimension of the ISM in the LMC bar region. However, it has been suggested that, despite a few exceptions, the fractal dimension is invariant from cloud to cloud, regardless of their nature as star-forming or quiescent, whether gravitationally bound or unbound (e.g., Williams et al. 2000).
Citation: Lee 2004
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The millimetre bump in M87 as recently observed by the Atacama Large Millimeter/submillimeter Array can be naturally modelled by the synchrotron emission of the thermal electrons in the ADAF, which is different from the prediction of the jet model. Therefore, it provides an opportunity to explore the accretion process near the BH horizon. In particular, Feng etal. (2016) and Li etal. (2016) both found that the rotation measure predicted from the ADAF is roughly consistent with the observational values. It is still difficult to constrain the BH spin parameter from the modelling of the SED of M87 due to some degeneracy in model parameters (wind parameter, s, magnetic parameter ; Feng etal. 2016). The spin parameter can be better constrained from the jet model if the relativistic jet is indeed powered by the rotating BHs as suggested by MHD simulations and some observations. We find that the dimensionless BH spin parameter should be larger than 0.96 for the lower limit of jet power derived from the X-ray cavities (e.g. Rafferty et al. 2006; Russell etal. 2013b). In this work, we adopt several typical values of parameters (e.g. 0.1, 0.3 and 0.5). The larger value of  will lead to a lower accretion rate near the horizon to explain the observed millimetre bump, and the BHs need to rotate faster to reproduce the observed jet power. The peak of synchrotron emission from the thermal electrons of ADAF will move to the submillimetre waveband if  is too small, which is different from the observed millimetre bump. We adopt the equipartition case of 0.5 in our calculations, where magnetic energy will become dominant if the BH is fast spinning, considering the possible amplification of the magnetic field by the frame dragging effect. For the weaker magnetic case (e.g. 0.5), the BH needs to rotate faster to explain the observed SED and jet power. We find that our results are not sensitive to the viscosity parameter . Therefore, we suggest that the BH should be fast rotating in M87 even after considering the possible uncertainties.
Citation: Feng etal. 2016)
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Citation: Feng etal. (2016)
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Traditionally, the Parker transport equation (Parker 1965) was used to model pickup ion acceleration at the SWTS when using a transport theory approach. However, interesting Voyager results, such as strongly fluctuating pitch-angle anisotropies upstream, the detection of highly anisotropic intensity spikes at the SWTS, the average upstream anisotropy peaking at a surprisingly high energy far above the upstream flow energy, and energetic particle spectra with multiple power-law slopes with breaking points in between that are highly variable upstream (Decker et al. 2005, 2008b; Cummings et al. 2006), suggest that transport modeling should be modified in at least two ways. First, the turbulent nature of the magnetic field conditions at the SWTS should be taken into account, and second, a transport theory that applies when particle distributions are highly anisotropic is needed, given that the Parker transport equation only holds for nearly isotropic particle distributions. In response, shock acceleration transport models were developed in recent years based on the numerical solutions of the focused transport equation (Kóta & Jokipii 2004; le Roux et al. 2007; Florinski et al. 2008a, 2008b; le Roux & Webb 2009) to take advantage of the fact that focused transport is not restricted to small pitch-angle anisotropies. This is especially advantageous at lower suprathermal particle energies upstream, where particle distributions can be sporadically strongly anisotropic (Decker et al. 2006), allowing one to model particle injection into diffusive shock acceleration (DSA) naturally at those energies. Furthermore, statistical variations in the observed magnetic field direction near the SWTS were included as a time series to model time variations in the injection and DSA of pickup ions to simulate the highly volatile nature of actual DSA at the SWTS (Florinski et al. 2008a, 2008b; le Roux & Webb 2009; Arthur & le Roux 2013). This focused transport approach should be seen as complementary to more sophisticated self-consistent shock acceleration models based on hybrid codes (Kucharek & Scholer 1995; Giacalone 2005) and particle-in-the-cell models (Scholer et al. 2003; Lembege et al. 2004), but it has the virtue of relative simplicity because different statistical plasma parameters can easily be studied separately and in combination at the SWTS to gain a more clear conceptional understanding of the role of such statistics on pickup acceleration at the SWTS.
Citation: le Roux & Webb 2009
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Citation: le Roux & Webb 2009
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In the previous sections, we have shown the difficulty in balancing follow-up time with large numbers of unconfirmed candidate companions from single-epoch photometry. This follow-up dilemma was the motivation for designing the K-peak filter: can we optimize the observing time required to determine the nature of a target? One answer is to use a carefully chosen combination of photometric filters. Using only photometry, we can calculate colours that contain information about the type of object being observed, allowing approximate characterization with single-epoch photometry. Past works (e.g. Najita, Tiede & Carr 2000; Allers & Liu 2020) have shown that custom photometric filters can be used to greatly improve the confirmation rate of photometrically selected candidate low-mass brown dwarfs. In previous work (Allers & Liu 2020; Jose et al. 2020; Dubber et al. 2021), we used a custom filter centred on the deep 1.45 µm feature present in YPMOs to distinguish between them and background sources. In the 2–5 µm range covered by NIX, such water features are far less dominant, and there are strong telluric features across some of this range that would make a similar ‘water’ technique difficult to use. Instead, we use the differing spectral shape in K-band of very low mass brown dwarfs when compared to earlier spectral type stars. This can be seen in the sample of spectra shown in Fig. 3. By locating filters at key spectral points for defining the overall shape of the spectra, the extracted colour information can be used for direct characterization. Also shown in Fig. 3 are spectra of well-studied brown dwarfs and exoplanets, discovered via direct imaging: 51 Eri b (Macintosh et al. 2015), β Pictoris b (Lagrange et al. 2010), PSOJ-318 (Liu et al. 2013), G196-3B (Rebolo et al. 1998), and HR 8799d and e (Marois et al. 2008, 2010). References for the spectral data plotted are detailed in the caption of Fig. 3. These spectra demonstrate the general variety in the spectral shapes of objects that have been detected via direct imaging previously, but also the similar features in the highlighted filter windows.
Citation: Dubber et al. 2021
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Cosmological hydro dynamical N-body simulations offer another possibility to investigate the origin of the bimodality in the ([Fe/H], [α/Fe]) plane. Earlier simulations, e.g. full N-body simulations by Loebman et al. (2011), Brook et al. (2012) or hybrid simulations in which a semi-analytic chemical evolution was added on top of a cosmological simulation (Minchev, Chiappini & Martig 2013, 2014), were able to show that the thin and thick discs lie along different tracks in the ([Fe/H], [α/Fe]) plane, with the thick disc being old metal poor and rich in [α/Fe] and the thin disc being young, metal-rich and poor in [α/Fe]. They also showed that migration was important to generate the two discs. However, a clear bimodality in the ([Fe/H], [α/Fe]) plane was not seen. In the past few years good progress has been made to improve the spatial resolution as well as the chemical enrichment prescriptions. The bimodality has now been observed in some simulations (Grand et al. 2018; Mackereth et al. 2018; Clarke et al. 2019), and some of the simulations, in addition to the bimodality, also reproduce the basic trends of the ([Fe/H], [α/Fe]) distribution with radius R (Buck 2020; Vincenzo & Kobayashi 2020). Unlike analytical models, such simulations cannot be fine tuned to reproduce the Milky Way data, hence, the focus of these simulations is to qualitatively reproduce the abundance trends seen in the Milky Way, to understand how frequently do we get the bimodality and what is the mechanism for it. However, there is a lack of consensus between the different studies. Clarke et al. (2019) and Buck (2020) suggest that bimodality should be common in disc galaxies, whereas Mackereth et al. (2018) suggest that it is rare. Each simulation suggests slightly different mechanisms for the existence of the bimodality. Clarke et al. (2019) attribute bimodality to vigorous star formation in clumps at high redshift. Grand et al. (2018) suggest two distinct pathways, a centralized starbust pathway induced by mergers and a shrinking gas disc pathway. Buck (2020) suggest that after the formation of the high-[α/Fe] sequence a gas-rich merger dilutes the metallicity of the ISM leading to the formation of the low-[α/Fe] sequence. Mackereth et al. (2018) attribute the bimodality to unusually rapid gas accretion at earlier times, which is also characterized by a short time-scale to convert gas to stars. While some simulations clearly identify migration as key process to shape the sequences, others do not. In spite of the differences, it seems that some of the simulations (e.g. Mackereth et al. 2018; Buck 2020; Vincenzo & Kobayashi 2020) are not inconsistent with the Schönrich & Binney (2009a) paradigm.
Citation: Mackereth et al. (2018)
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Citation: Mackereth et al. 2018
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Citation: Mackereth et al. (2018)
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To test IADI, we made a model of a disk in scattered light. This model is 400 × 400 pixels, each pixel having a physical size of 1 × 1 au. Multiple rings are implemented, inspired by observations of disks (e.g., HD 97048 Ginski et al. 2016; RX J1615 de Boer et al. 2016 and TW Hydrae van Boekel et al. 2017), see panel a in Fig. 2. A specific inclination and rotation is achieved using the warpAffine function from OpenCV (Bradski 2000). Young planet-forming disks are still gas rich and dust particles are stratified due to gas pressure along the vertical axis. Therefore, flaring is implemented in the model via an offset of the rings with respect to the center of the ring (see the detailed discussion in de Boer et al. 2016). For our disk model, we are using the power-law profile for the scattering surface height H and the separation r found by Ginski et al. (2016) for the disk around HD 97048. This profile describes the flaring discovered in this source reasonably well up to a separation of ~270 au. Considering that the model disk used will have a separation of 200 au, this formula will be sufficient to simulate the disk height2. Illumination effects of the central star are implemented via a ~1/r2 intensity dependence from the center of the disk, making the inner part of the disk brighter compared to the outer part. Moreover, the intensity also depends on the light scattering angle via the phase function. Because a physical model is beyond the scope of this work, a “pseudo” phase function is implemented to mimic the same asymmetries in light distribution seen in observed disks via I = cos ϕ, where the intensity I depends on the cosine of the azimuthal angle ϕ. This pseudo phase function depends on the azimuthal angle instead of a scattering angle on which a real phase function would depend. This makes the part of the disk facing toward the observer appear brighter than the part facing away in a fairly simple way. Lastly, the model is put through a Gaussian convolution kernel from the scipy ndimage package (Virtanen et al. 2020) to remove sharp edges and give a finite resolution to the model. The final three steps are shown in the top row of Fig. 2.
Citation: Ginski et al. (2016)
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However, we note that the quoted eccentricity for the inner planet in HD 155358 is 0.17 ± 0.03 (Robertson et al. 2012a). The mean value is exceeded for the standard run at 4000 orbits after going into resonance with the implication that the planets could not have been in resonance longer than this time. If this is the case, the above discussion would have to be modified to allow the planets to migrate independently from larger radii before converging on to resonance close to their final locations. This is likely to need to be considered for different possible exterior disc models and in addition the planets may have built up their masses as they went (see e.g. Tadeu dos Santos et al. 2015). These considerations are beyond the scope of this paper. None the less, because the migration rates for single planets and the resonantly coupled planets are in general similar, the estimated starting radii would also be similar for disc models that are similar to those we considered. But note that the attained eccentricities depend on the eccentricity damping rates which depend on the details of the disc model (see Crida et al. 2008). For example, we found that for the same amount of relative resonant migration, the entirely inactive disc model led to smaller eccentricities while the 3D layered model led to larger eccentricities. Thus, it is important to note that there is uncertainty as to how long the planets could have been in resonance. In the same context, we comment that migration in the completely active disc model was slower by a factor of ∼1.6 compared to the standard case on account of its lower mass, that being determined so as to maintain the same steady-state accretion rate as in the standard case. Furthermore, the potential importance of a residual inner gaseous disc for damping the eccentricity of the inner planet and so preventing the eccentricities of both planets from continuing to increase in the later stages of the orbital evolution has been stressed by Crida et al. (2008). In addition, Murray, Paskowitz & Holman (2002) indicate that a residual disc of planetesimals could produce a similar effect.
Citation: Tadeu dos Santos et al. 2015
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