adsabs/FOCAL huggingface dataset:
Since it is infeasible to observe the evolution of individual galaxies over time, theoretical approaches focusing on understanding the formation of ETGs have made use of numerical simulations to trace the evolution of individual galaxies. Through zoom-in and cosmological simulations, a consensus has emerged between these simulations that the formation of ETGs proceeds through two phases, where galaxies first go through dissipative gas-rich wet mergers followed by in situ star formation bursts at redshifts above z ≈ 2, and then evolve towards low redshift through non-dissipative gas-poor dry mergers (Naab et al. 2007; Guo & White 2008; Hopkins et al. 2009; Nipoti et al. 2009b; Nipoti, Treu & Bolton 2009a; Oser et al. 2010; Johansson, Naab & Ostriker 2012; Moster, Naab & White 2013; Remus et al. 2013; Furlong et al. 2015; Wellons et al. 2015, 2016; Rodriguez-Gomez et al. 2016). However, regarding the redshift evolution of ETGs’ total power-law density slopes, no consensus has been reached neither among different cosmological hydrodynamic simulations nor between simulations and observations, despite the many advances in cosmological simulations (Vogelsberger et al. 2019a). While the Magneticum pathfinder simulation (Remus et al. 2017) and the Illustris simulations (Xu et al. 2017) produce shallower total density profile with time, the Horizon-AGN simulations (Peirani et al. 2019) produce steeper total density profile with time, in better agreement with the redshift evolution trend found in observations. However, the latter simulation has smaller slope values compared to the former two, which are closer to the observed slope values due to different implementation of feedback models, etc. Apart from cosmological simulations, dedicated zoom-in simulations (Johansson, Naab & Burkert 2009; Johansson et al. 2012; Remus et al. 2013) have revealed that dry mergers that dominate the passive evolution of ETGs below z ≈ 2 could make the total density profile shallower than isothermal (Hilz et al. 2012; Hilz, Naab & Ostriker 2013; Remus et al. 2017). The inclusion of wet mergers is also crucial for reconciling the simulated redshift evolution trend of the slope with strong-lensing observations (Sonnenfeld, Nipoti & Treu 2014).
Citation: Remus et al. 2013
Citation function: Background
On the basis of its unusual morphology and steep spectrum, we suggest that R1 traces the synchrotron radiation from a wind launched from the accretion flow around the SMBH. Known as the ADAF wind, such an energetic outflow is a generic prediction by theories and numerical simulations of hot accretion flows in LLAGNs (Blandford & Begelman 1999; Yuan et al. 2012a, 2012b, 2015). Originating from the corona region of the hot accretion flow, the ADAF wind would have a wide opening angle compared to the highly collimated jet, thus more naturally matching the observed radio morphology. Relativistic electrons responsible for the synchrotron may be produced in the corona of ADAF, where frequent magnetic reconnection is expected to occur (Yuan et al. 2009). Alternatively, relativistic electrons may be produced via the outward propagating wind shocking the circumnuclear medium over a region of 10 pc, corresponding to ∼4 × 107Rs (here 





 is the Schwarzschild radius of the putative SMBH). To our knowledge, there has been no dedicated theoretical effort to predict the observational signatures of an ADAF wind on relevant physical scales. Here we shall provide a simple estimate of the energetics related to the radio emission. First, we notice that the radiative efficiency of an ADAF (





) at highly sub-Eddington rates scales roughly with the net mass accretion rate (i.e., the mass inflow rate into the event horizon) as 





 (Xie & Yuan 2012), where Lbol is the bolometric luminosity of the SMBH and 





 is the Eddington accretion rate. Assuming that the 2–10 keV luminosity of X1 (





) is 10% of the bolometric luminosity, and adopting a canonical value of δ = 0.5, the fractional viscous heating of electrons in the ADAF,11

11
See Xie & Yuan (2012) for discussions about this parameter; adopting a smaller value of δ would result in a higher 





.
 we may infer 





. Numerical simulations show that the ADAF wind carries a kinetic energy of 





 (Yuan et al. 2015), which is then 





 according to the above estimate. This value is sufficient to account for the observed synchrotron radiation, given that typically 1% of the kinetic energy goes to shock-accelerating relativistic electrons (Nims et al. 2015). The ADAF wind may also have produced soft X-ray emission upon shock-heating the circumnuclear medium. We note that the observed 0.5–2 keV luminosity from X1 (





; Y15) is compatible with the estimated wind kinetic energy.
Citation: Yuan et al. 2009
Citation function: Background
Even if non-accreting millisecond pulsars are unaffected by α-oscillations, some NSs in LMXBs (accreting millisecond pulsars), which are attached to sharper peaks centred at higher temperatures, still can be unstable with respect to α-oscillations and thus should exhibit them. To check this possibility, we need more X-ray observations with high temporal resolution that can be achieved by future missions such as LOFT (Feroci et al. 2012), NICER (Gendreau, Arzoumanian & Okajima 2012) and SRG (Merloni et al. 2012). Another opportunity to detect α-oscillations can be associated with the recently discovered transitional millisecond pulsars, which are switching between rotation- and accretion-powered states [currently, three of such objects are known: PSR J1023+0038 (ν ≈ 592.4 Hz, Archibald et al. 2009; Stappers et al. 2014), IGR J18245−2452 (ν ≈ 254.3 Hz, Papitto et al. 2013), and XSS J12270−4859 (ν ≈ 593.0 Hz, Roy et al. 2015)]. In a rotation-powered state these NSs are observed as radio pulsars, giving a chance to study their timing properties precisely. The accretion-powered state guarantees that these pulsars cannot be too cold, and thus likely to be affected by r-mode instability and α-oscillations. For example, the redshifted surface temperature of PSR J1023+0038 can be as large as 5 × 105 K (Homer et al. 2006; Bogdanov et al. 2011). It corresponds to the internal temperature T∞ = (1.5–3) × 107 K, implying that PSR J1023+0038 can be on the stability peak. Its timing behaviour was reported as ‘complex’ by Archibald et al. (2013) and can be influenced by α-oscillations. The reliable registration of α-oscillations would confirm the crucial role of the resonance interaction of oscillation modes in the evolution of NSs in LMXBs (Gusakov et al. 2014a,b). The measurements of α-oscillation parameters (such as the period of oscillations, $\widehat{P}$) would provide us with a new powerful tool to constrain the properties of superdense matter.
Citation: Gendreau, Arzoumanian & Okajima 2012
Citation function: Future Work; Future Work
Despite significant advances, the solar wind remains challenging to model (and importantly forecast) due to the large range of scales that need to be incorporated. It is well known that energy is injected into the corona at all scales, from magnetohydrodynamic (MHD) waves (Nutto et al. 2012; Van Doorsselaere et al. 2020) to flares and coronal mass ejections with global extent (Aschwanden et al. 2017; Green et al. 2018; Wyper et al. 2018). In addition to the range of spatial scales, heating events take place on a variety of timescales (Hollweg 1973; Viall & Klimchuk 2017). Putting aside large-scale eruptions so as to focus on the quasi-steady input of energy into the corona, it is possible to distinguish three broad magnetic configurations of the solar atmosphere for modelling purposes. These are the quiet Sun (Danilovic et al. 2010; Rempel 2014), active or enhanced regions (Carlsson et al. 2016; Chen et al. 2021), and coronal holes (Wójcik et al. 2019). In each of these configurations, energy is channelled from the convection into the low corona. Coronal holes are the dominant source of the solar wind in the heliosphere (Cranmer et al. 2017; Stansby et al. 2021), typically producing the fast solar wind (McComas et al. 2008; Ebert et al. 2009; Macneil et al. 2020a; Wang 2020). The magnetic field configuration of a coronal hole is relatively simple, compared with the quiet Sun and active regions, given that the field is principally open to the solar wind (Lowder et al. 2017; Hofmeister et al. 2019). However, there are still a range of dynamic processes taking place, such as the braiding of magnetic field lines (Wedemeyer-Böhm et al. 2012; Wedemeyer et al. 2013; Huang et al. 2018) and the emergence of new magnetic flux (Murray et al. 2009). These can trigger the formation of jets (Shen et al. 2017; Yang et al. 2017) and other phenomena, which are then observed as spicules (Martínez-Sykora et al. 2017; Bose et al. 2021) or fibrils (Hansteen et al. 2006; Leenaarts et al. 2015).
Citation: Rempel 2014
Citation function: Background
The TESS light curves present the best cadence of the datasets available for CR Cha. They cover 56 days. The 2019 TESS light curve (Fig. 1, middle panel), taken with a 30 min cadence, reveals both periodic and stochastic variations with a peak-to-peak amplitude of ∼0.04 mag. In order to find periodic signals in these photometric data, we computed the Lomb-Scargle periodogram (Lomb 1976; Scargle 1982), which is shown in the bottom panel of Fig. 1. The periodogram resulted in significant peaks at 2.314 ± 0.033 days and 1.156 ± 0.009 days. An analysis of the more recent TESS light curve taken with a 10 min cadence between 2021 April and June (Fig. 2), obtained with the same analysis technique as for the 2019 data (see Sect. 2.3), confirms the presence of these two peaks. The peak at 2.314 days is consistent with the stellar rotational period found in previous studies (P = 2.3 days; Bouvier et al. 1986), suggesting the presence of starspots on the stellar surface. The 1.156-day period is half the stellar rotational period. The TESS light curve also indicates a long-term oscillation with a timescale of ∼25 days, but the time coverage of our dataset is not long enough to probe these longer timescales. Robinson et al. (2021) modeled the effect of inclination on how periodic a light curve appears. They found that higher inclinations lead to more burst-dominated light curves. The moderate 31° inclination of CR Cha indicates that the light curve is expected to be not purely periodic. We were able to detect a rotational period in our analysis of the light curve, but at the same time, additional effects, such as accretion variation and flares also significantly contribute to the observed light curve. The TESS light curve shows a few flare-like events as well, indicated by the blue points in the middle panel of Fig. 1. During the 56-day-long TESS observing period in 2019, we found five flares with durations ranging from 3.8−7.2 h, which gives a flare rate of 0.09 days−1. However, these were events showing ∼0.02 mag brightening.
Citation: Bouvier et al. 1986
Citation function: Similarities
In the σ0–SFR plane, local (U)LIRGs and high-z galaxies tend to occupy the same region, regardless of their intrinsic difference in terms of morphology, gas fraction and starburstiness. In order to distinguish between normal MS and SB galaxies, we show in Fig. 7, bottom, the velocity dispersion σ0 normalised to σ0, MS (solid line in the top panel; Übler et al. 2019) as a function of the starburstiness δMS = sSFR/sSFR|MS for all targets already reported in the previous panels. The sSFR|MS is derived from the Speagle et al. (2014) relation, starting from the available stellar mass measurements from the literature for Johnson et al. (2016), Förster Schreiber et al. (2018), Molina et al. (2020), Cochrane et al. (2021), and KMOS3D individual targets; stellar masses of LIRGs and ULIRGs from this study, from Bellocchi et al. (2013), Pereira-Santaella et al. (2019) and Crespo et al. (2021), are instead derived from the dynamical mass estimates, assuming M* = (1 − fgas)×(1 − fDM)×Mdyn, where fgas and fDM are the gas and dark matter fractions4, respectively, and Mdyn is the dynamical mass within 2Re (see next section). For the gas fraction, we considered a conservative fgas = 0.1 (Isbell et al. 2018; higher fgas values would further increase their δMS). This gas fraction is consistent with the estimate we obtain considering the molecular gas mass inferred by ALMA data (Paper II; Lamperti et al., in prep.) and the M* measurements available for a few PUMA targets (see Table 2), 






f


¯


gas

=
0.11
±
0.05


$ {\bar{f}}_{\mathrm{gas}} = 0.11 \pm 0.05 $


. For the dark matter fraction (within 2Re) we assumed fDM = 0.26, defined as 1 − Mbar/Mdyn, with Mbar = M* + Mgas = M*/(1 − fgas). The fDM estimate was derived for the PUMA and Johnson et al. (2016) SB galaxies for which stellar masses are available, and considering the dynamical masses within 2Re (see Sect. 3.7). Because of these assumptions, we considered a factor of 3 uncertainties for the stellar mass measurements of (U)LIRGs. These uncertainties, however, play a minor role in the derived δMS: at low z, the MS has a soft slope, and normal and massive MS galaxies have similar SFRs (e.g. at z ∼ 0.1, galaxies of 1010 and 1011 M⊙ have SFR|MS ∼ 1 and ∼3.5 M⊙ yr−1, respectively); on the other hand, local (U)LIRGs have much higher SFRs, from 10 s to 100 s M⊙ yr−1, and therefore δMS of the order of 10 − 100. Finally, for the Alaghband-Zadeh et al. (2012) and Harrison et al. (2012) high-z galaxies we assumed that M* = 1011 M⊙, following Harrison et al. (2012).
Citation: Molina et al. (2020)
Citation function: Uses; Uses
Regardless of the process or combination of processes responsible for generating the emission, the kinetic energy of the returning debris must eventually be dissipated in order to be observed. Even if some energy is deposited by circularization at large distances (Piran et al. 2015), the energy will be primarily dissipated by processes that operate closest to the black hole simply because the velocities there are the greatest. However, this would imply that most of the radiation would be emitted at very high energies (X-rays), and instead we observe many TDEs with significant (and sometimes dominant) optical/UV flux. A reprocessing layer, either static or outflowing (Miller 2015; Metzger & Stone 2016), can help explain the observed emission by reprocessing the luminosity generated by the various dissipation processes at play (Loeb & Ulmer 1997; Ulmer et al. 1998; Bogdanović et al. 2004; Guillochon et al. 2014; Jiang et al. 2016; Coughlin & Begelman 2014; Strubbe & Quataert 2009). The reprocessing of the radiation has also been used to successfully explain the line ratios observed in PS1-10jh (Gaskell & Rojas Lobos 2014; Roth et al. 2016). In this work we assume a simple blackbody photosphere for the reprocessing layer, so that the observed flux becomes
8





with an effective blackbody temperature
9





In the above equations, Fν is the specific flux, Rphot is the photospheric radius, D is the distance from the source, L is the bolometric luminosity from our fit, and Teff is the temperature of the photosphere. Most observations of TDEs have thermal temperatures that do not exhibit significant variation. For blackbody emission, the radius must increase as the luminosity (and 





) increases and decrease as the luminosity decreases, in order for the temperature not to change significantly as the luminosity evolves. This simple behavior also explains the rise in temperatures at late times as the photospheric radius decreases and the bulk of the observed radiation shifts to higher energies. To model this dependence, we assume that the radius of the photosphere has a power-law dependence on the luminosity and fit for both the power-law exponent l and radius normalization Rph0,
10





Here ap is the semimajor axis of the accreting mass at peak 





. This provides a reasonable typical scaling for the radius of the photosphere, with a minimum photosphere size set by Risco and a maximum photosphere size set by the semimajor axis of the accreting mass.
Citation: Jiang et al. 2016
Citation function: Uses; Uses
Feature VY may not be a dusty clump in the same sense as the other features since it has a significant flux contribution from the star. In our model, VY is modeled as dusty medium of a Gaussian density distribution centered on VY CMa and uniformly surrounding the star, as in an idealized spherical outflow. Alternatively, however, it could be a clump located in front of or behind the star and seen along the same line of sight. In our implementation, we clear the central space of dust within a radius of 90 AU (equivalent to a diameter of 



0

.
″

15


$ 0{{\overset{\prime\prime}{.}}}15 $


) where solids would be too warm to exist heated by the cool but very luminous star. The peak surface brightness simulated with a beam of 152 mas are 34 and 140 mJy beam−1 in Bands 6 and 7, respectively. Of that, 13.0 and 30.1 mJy beam−1 comes from the blackbody radiation of the model star. O’Gorman et al. (2015) noted that stellar fluxes are underestimated in a blackbody model at millimeter and submillimeter wavelengths owing to the unaccounted presence of an extended radiophotosphere. We extrapolated fluxes expected from their radiophotosphere model getting 12 mJy in Band 6 and 44 mJy in Band 7. Given the small difference with our assumed blackbody fluxes and uncertainties in the radiophotosphere model of O’Gorman et al. (2015), we did not correct our model for this effect. We required 0.014 M⊙ of dust to explain the observed fluxes which is rather large compared to most other clumps (except C). The stellar contribution partially explains the very low spectral index of 1.7 toward VY because the stellar spectrum is expected to have α ≈ 2 (Lipscy et al. 2005). At the modeled parameters of VY, the medium must be very thick at visual light and obscure the stellar photosphere from a direct view for a terrestrial observer, as visual observations indeed seem to indicate. The stellar photosphere and its light variations are seen mainly through light scattered in the surrounding medium with a high degree of inhomogeneity (Humphreys et al. 2005).
Citation: O’Gorman et al. (2015)
Citation function: Uses
Citation: O’Gorman et al. (2015)
Citation function: Compare/Contrast; Compare/Contrast
Small grains, which contribute most to infrared emission, are removed by collisional fragmentation and blown out by radiation pressure. The removal timescale is much shorter than the ages of host stars. Disruptive collisions among underlying large bodies, which are called planetesimals, produce smaller bodies and collisional fragmentation among them results in even smaller bodies. This collisional cascade continues to supply small grains. The evolution of debris disks has been explained by the steady-state collisional cascade model (e.g., Wyatt 2008; Kobayashi & Tanaka 2010): the total mass of bodies decreases inversely proportional to time t. Therefore, the excess ratio (Fdisk/F∗) is given by (2)\begin{equation} \frac{F_{\rm disk}}{F_{*}} = \frac{t_0}{t},\label{cc} \end{equation}FdiskF∗=t0t,where t0 is the dissipation timescale that is determined by the collisional cascade. Under the assumption of the steady state of collisional cascade, the power-law size distribution of bodies is analytically obtained and the power-law index depends on the size dependence of the collisional strength of bodies (see Eq. (32) of  Kobayashi & Tanaka 2010). In the obtained size distribution, erosive collisions are more important than catastrophic collisions (see Fig. 10 of Kobayashi & Tanaka 2010). Taking into account the size distribution and erosive collisions, we derive t0 according to the collisional cascade (see Appendix E for derivation), (3)\begin{eqnarray} t_0&amp;\sim&amp; 1.3 \left( \frac{s_{\rm p}}{\rm 3000\,km} \right)^{0.96} \left( \frac{R}{\rm 2.5\,au} \right)^{4.18}\nonumber\\ &amp;&amp;\quad\times \left(\frac{\Delta R}{0.4 R}\right) \left( \frac{e}{\rm 0.1} \right)^{-1.4} {\rm Gyr},\label{eq:t0} \end{eqnarray}t0~1.3sp3000 km0.96R2.5 au4.18where sp is the size of planetesimals, R is the radius of the planetesimal belt, and e is the eccentricity of planetesimals. Interestingly, t0 is independent of the initial number density of planetesimals (Wyatt et al.  2007). Note that the perturbation from Moon-sized or larger bodies is needed to induce the collisional fragmentation of planetesimals (Kobayasi & Löhne 2014), which is implicitly assumed in this model.
Citation: Kobayashi & Tanaka 2010
Citation function: Background; Background
Molecular clouds can be formed either directly from neutral medium or by assembling pre-existing, cold molecular clumps. The first scenario is commonly accepted (e.g., Hollenbach & McKee 1979). The second scenario is outlined by Pringle et al. (2001), who proposed that clouds formed out of pre-existing, CO-dark, molecular gas. Compared to the first scenario, the second scenario allows for fast cloud formation in a few Myr, which was suggested by observations (Beichman et al. 1986; Lee et al. 1999; Hartmann et al. 2001). The key problem for the second evolutionary scenario is how molecular gas can exist before it is collected together. Pringle et al. (2001) argued that the pre-existing molecular gas should be cold (10 K) and was shielded from photodissociation by patch dust with AV of ~0.5 mag (White et al. 1996; Berlind et al. 1997; González et al. 1998; Keel & White 2001). Under AV of ~0.5 mag, H2 should exist substantially while CO is in very low abundance. This is because the self-shielding threshold of AV = 0.02 and 0.5 mag (Wolfire et al. 2010) are widely considered as a condition for maintaining a stable population of abundant H2 and CO gas, respectively. Listz & Pety (2012) detected strong CO(1−0) emission (4−5 K) in regions with equivalent visual extinction less than 0.5 mag. Two obvious possibilities are 1) the CO gas is transient. Such gas may also have been seen in Goldsmith et al. (2008), who detected a 40% of the total CO mass in Taurus in regions with low to intermediate AV (mask 0 and 1 in their terminology). 2) Such CO gas lies in a highly clumpy medium with lower apparent averaged extinction when photons travel through lower density interclump medium. When small agglomerations of molecular gas are compressed and heated by shock, e.g., in a spiral arm, they become detectable. This scenario is supported by observations of GMC formation in spiral arms (Dobbs 2008) and simulations of molecular clouds formation (e.g., Clark et al. 2012).
Citation: González et al. 1998
Citation function: Background