adsabs/FOCAL huggingface dataset:
A question of relevance to life on other planets is whether the Snowball catastrophe occurs at lower radiative forcing at low or high obliquity. There is some conflict in the literature on the role of the Snowball bifurcation in planetary habitability. On the one hand, defining habitability in terms of surface liquid water suggests that a Snowball climate is uninhabitable, which has led various authors to propose metrics of fractional or seasonal habitability for planets with partial ice cover (e.g., Williams & Pollard 2003; Spiegel et al. 2008). On the other hand, not only did photosynthesis persist through Snowball events in Earth’ history, but the events may have crucially shaped the subsequent evolution of complex life (e.g., Hoffman & Schrag 2002; Hoffman et al. 2017; Laasko & Schrag 2017). The traditional habitable zone concept assumes a planet with a functioning silicate-weathering feedback and a positive CO2 greenhouse effect (Kasting et al. 1993; Kopparapu et al. 2013). Global glaciation may be triggered on such a planet through a rapid drawdown of atmospheric CO2 that reduces q below the thresholds at which the non-Snowball states disappear. In Earth history, this seems to have occurred through accidents of tectonics (Hoffman et al. 2017). These events are self-terminating through the suppression of silicate weathering and accumulation of a strong CO2 greenhouse. Such transitions can in principle occur anywhere within the habitable zone. Our simple model is unsuited to the tasks of diagnosing the inner edge of habitability (where the relevant physics are the runaway water vapor greenhouse and hydrogen loss to space) or the outer edge (where the relevant physics are CO2 condensation and Rayleigh scattering). However, we can compute the q value at which the bifurcation occurs as a function of obliquity and other model parameters, and whether the transition into the Snowball state occurs from a partially ice-covered state (cap or belt) or directly from the ice-free state.
Citation: Kopparapu et al. 2013
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In Fig. 5, we consider how the velocity dispersion profile scales with radius. Specifically, we plot the power-law index (η) versus the central velocity dispersion (σ0). The vast majority of the galaxies with σ0 ≲ 2.45 are BGGs and these have negative η values. This includes most of the Romulus galaxies (red filled and open circles), the L18 BGGs (blue crosses) and the early-type galaxies that comprise the SAURON sample (Cappellari et al. 2006; grey line and shaded area). In contrast, nearly all of simulated BGGs with σ0 ≲ 2.45) from the DIANOGA Hydro-10x simulations Marini et al. (2021) have positive η values. For σ0 ≳ 2.45, the spread of η for the observed galaxies (e.g. L18 and Newman et al. 2013 BCGs) broadens and spans both positive and negative η values. In fact, majority of the galaxies tend to have positive ηs. This change in behaviour is well known. A number of studies have noted that on the group-scale and lower, the stellar velocity dispersion profile of the central galaxies tend to decrease with increasing radius. On the cluster-scale, the BCGs typically have rising velocity dispersion profiles with increasing radius (Von Der Linden et al. 2007; Bender et al. 2015; Veale et al. 2017). The origin of this flip is still not well understood. We leave a more detailed investigation of this change to future work. Here, we simply mention two possible explanations: The change in slope may be a reflection of the differences in the dynamical state (e.g. mass-to-light ratio; M/L) at the outskirts of BCGs (Dressler 1979; Fisher, Illingworth & Franx 1995; Sembach & Tonry 1996; Carter et al. 1999; Kelson et al. 2002; Loubser et al. 2008; Newman et al. 2013; Schaller et al. 2015; Marini et al. 2021), or it could be due to increased contribution from the intragroup/intracluster light along the line-of-sight and the increased leverage of tangential orbits (Loubser et al. 2020). All of these effects are linked to the increased frequency of galaxy–galaxy interactions and more specifically, central-satellite interactions, implicated in the build-up of extended diffuse stellar component. And, as discussed by Schaye et al. (2015), Oppenheimer et al. (2021), and the EAGLE simulations clearly show that the extended stellar halo becomes increasingly more important, and hosts a non-trivial fraction of the total stellar mass towards the cluster scale.
Citation: Newman et al. 2013
Citation function: Future Work
Citation: Newman et al. 2013
Citation function: Compare/Contrast; Compare/Contrast
The mechanisms of CL-AGNs are still debated. Early explanations mainly focus on the change in obscuration (Bianchi et al. 2005), while some recent studies favor the change of accretion rate (Stern et al. 2018; Sniegowska et al. 2020). Photoionization research shows that the CL behavior in CL quasars can be fully explained by the photoionization responses of the BELs to the extreme variability of the ionizing continuum (Guo et al. 2020). Besides, several objects can be interpreted as tidal disruption events (Merloni et al. 2015; Ricci et al. 2020). All of the models can generate significant variance in the observed intensity of continuum and emission lines. These models might be restricted by statistical studies after extending the sample of CL-AGNs. Several groups have been devoted to searching for new CL-AGNs, and tens of candidates have been discovered based on large optical and X-ray surveys, such as the Sloan Digital Sky Survey (e.g., LaMassa et al. 2015; MacLeod et al. 2016; Rumbaugh et al. 2018; Hon et al. 2020), intermediate Palomar Transient Factory (Gezari et al. 2017), Pan-STARRS1 (MacLeod et al. 2019), Catalina Real-time Transient Survey (Yang et al. 2018), and XMM-Newton slew survey (Zetzl et al. 2018; Kollatschny et al. 2020). In the future, the Large Synoptic Survey Telescope will regularly monitor millions of AGNs, and the number of CL-AGNs will further increase. The evolution of the AGN type can be well studied if we obtain continuous multiwavelength data of the complete changing process, and the development of time-domain surveys makes it possible to achieve this. The brightening of NGC 2617 (Shappee et al. 2014) and 1ES 1927+654 (Trakhtenbrot et al. 2019) triggered the alert of the All-Sky Automated Survey for Supernovae,7

7

http://www.astronomy.ohio-state.edu/asassn

 and follow-up observations confirmed these two CL-AGNs. However, so far, there are only small available data sets for most detected CL-AGNs, especially around the critical regime of transition between types 1 and 2. Moreover, previous studies rarely gave the change of geometry and kinematics in broad-line regions (BLRs).
Citation: Hon et al. 2020
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The level of solar activity is typically represented by the number or area of sunspots. On the other hand, the “rush-to-the-pole” behavior of PCFs is believed to be one of the precursors for the next solar maximum (Altrock 2014). Figure 3 shows the maximum sunspot numbers (SILSO World Data Center 1914–2020) in comparison with the migration speed of PCFs (orange dots) in the northern and southern hemisphere for cycles 16–24. Within these nine solar cycles, the maximum sunspot number varies in two large periods, cycles 16–20 and 20–24. Note that even in longer time spans, such as from cycle 1 to 24, the sunspot number seems to vary periodically in different periods, like the often-discussed Gleissberg cycle (Le Mouël et al. 2017). The migration speeds of PCFs in the northern hemisphere can be separated into two groups, before and after cycle 20, which is similar to the trend found in the variation of sunspot numbers. However, the migration speed in the southern hemisphere is not correlated with the sunspot number and the migration speed in the northern hemisphere. As shown in Table 2, the Pearson correlation coefficients are below 0.5 in both hemispheres, in which the value for the northern hemisphere is relatively higher. To investigate the trend of temporal variation, the derivatives of migration speeds and sunspot number are calculated. The correlation between these derivatives represents the relationship between the temporal trends. It shows that the periodical variation of PCF migration in the northern hemisphere correlates better with the variation of sunspot numbers, but the absolute values of coefficients are not high (∼0.5). Based on this result, a causal connection between the temporal variations of PCF migration and the maximum number of sunspots cannot be established. In the SILSO World Data Center (1914–2020) archive of sunspot numbers, clear NS asymmetry after cycle 20 is noticed. But the overall levels of the sunspot numbers are the same in the northern and southern hemispheres. Therefore, the NS asymmetry, which is relatively small compared to the absolute sunspot number, will not affect the results shown in Figure 3.
Citation: Le Mouël et al. 2017
Citation function: Background
Our analysis on the LAMOST-RC stars by dissecting the MAPs shows that the chemical bimodality is observed throughout the Galactic disk, and the high- and low-[α/Fe] sequences are corresponding to the thick and thin disks of the Milky Way, respectively. How to explain the formation mechanism of the stellar thin and thick disks is beyond the scope of this paper, but our results provide some observational constraints to the model of the chemodynamical evolution of the Milky Way disk. Our flared vertical profiles for the thin and thick disks are in good agreement with the prediction of the thin+thick flaring disk model (López-Corredoira & Molgó 2014), and are consistent with the number simulations of the chemodynamical evolution in Galactic disks formed in the cosmological context (Minchev et al. 2013, 2014, 2015, 2017), as well as the cosmological zoom simulation of VINTERGATAN (Agertz et al. 2021). These model simulations suggest that the vertical flaring trends are a natural consequence of inside-out, upside down growth coupled with disk flaring (see also Bird et al. 2013; García de la Cruz et al. 2021), which allows for the low-[α/Fe] stars to exist several kpc above the disk’s midplane. As analyzed by B16, the exponential flaring profiles for the low-[α/Fe] MAPs suggests that radial migration played an important role in the formation and evolution of the thin disk. Radial migration of stars via cold torquing, also known as “churning,” by a bar and spiral waves (Minchev et al. 2013) then allows for the populations to spatially overlap in the solar neighborhood. Similar to the flared thin disk, the flaring profile for the high-[α/Fe] MAP indicates the radial migration has occurred in the formation of the thick disk as suggested by model simulations (e.g., Schönrich & Binney 2009; Minchev et al. 2015; Li et al. 2018). Of course, we cannot rule out the other formation scenarios of the thick disk, such as the accreted gas from satellites (Brook et al. 2004), accreted stars from galaxy mergers (Abadi et al. 2003), or from disk-crossing satellites heating up the thin disk (Read et al. 2008). On the other hand, the broken exponential radial profiles for the thin and thick disks cannot be explained by any model of the galactic disks. In fact, nearly all the models we mentioned above present a single-exponential profile decreasing with the increasing of R (e.g., Minchev et al. 2015; Li et al. 2018; Agertz et al. 2021). And the smooth downtrend of radial profile in the outer disk R > Rpeak, as shown in Figure 10, means that there is no cut-off of the stellar component at R = 14–15 kpc as stated by Ruphy et al. (1996), which is also discovered by B16.
Citation: Minchev et al. 2013
Citation function: Similarities; Similarities
Citation: Minchev et al. 2013
Citation function: Uses; Uses
The choice of SFH to infer evolutionary parameters intrinsically carries a degeneracy with the adopted functional form. A more direct approach is to quantify the light-weighted contribution of recent star formation by measuring the relative contribution to the integrated stellar spectrum of short-lived massive stars with respect to long-lived lower-mass stars. Balmer absorption lines reach their maximum strength in A-type stars with a spectral break at 3646 Å. Stars of lower mass and lower effective temperature produce metal absorption lines (CaII H & K, Fe, and Mg) which result in a sharp spectral break at 4000 Å. Moreover, the underlying continuum changes shape with time, progressively losing emission in the NUV-blue spectral range while flattening in the NIR. The different evolutionary rates of the stars producing the lines and their fractional contribution to the optical light at fixed mass enable tracing the evolutionary stage of a galaxy. In Fig. 11 (upper panels) we show the evolution of the spectral break measured through the DB definition (Kriek et al. 2006) and the Dn4000 definition (Balogh et al. 1999), as well as the relative strength (the ratio) between the two (lower left panel). Lighter shaded curves show the variation with increasing duration of star formation. The ratio is shown as a function of age of composite templates built with a short truncated SFH. The ratio is only mildly dependent on dust reddening because the two indices share a similar wavelength range. Moreover, the two indices are fairly robust against low resolution. The ratio varies strongly during the first 1 Gyr or so, reaching its maximum around 0.3–0.5 Gyr. Eventually, it drops below 1 when the light-weighted contribution from A-type stars fades away. Constant star formation instead results in a ratio of ∼1.1 that is rather constant with time. Varying the metallicity of the input templates has the effect of anticipating the transition to DB/Dn4000 > 1. This effect is strongest when supersolar metallicity (2.5Z⊙) is adopted in which case the transition is reached at 0.9 Gyr. We suggest that this ratio could be used to spot PSB galaxies with high dust attenuation along and across the UVJ diagram when high-resolution spectra are unavailable.
Citation: Kriek et al. 2006
Citation function: Uses; Uses
The role of cluster mergers in producing shocks underlying radio relics at cluster peripheries is well supported by observational evidence (Giovannini, Tordi & Feretti 1999; Bagchi et al. 2006; van Weeren et al. 2009; Venturi et al. 2013). A different class of relics, proposed to be fading radio galaxy lobes or such lobes revived due to adiabatic compression (Enßlin & Gopal-Krishna 2001), are also well known to occur in clusters. Examples of relics in this category are those in Abell 4038 (Slee & Roy 1998; Kale & Dwarakanath 2012) and Abell 85 (Slee et al. 2001). In A168 we find an arc-like relic at the periphery (B) and a smaller steep-spectrum relic (A) in its wake, as seen projected in the plane of the sky. The intracluster medium (ICM) in the cluster is elongated along the north–south direction and the orientation of B is perpendicular to this direction. Merger-shock-related relics have been found to be oriented preferentially perpendicular to the elongation axis of the ICM (van Weeren et al. 2011). The flat spectral index of B, arc-like morphology and orientation thus support the scenario that a cluster merger along the north–south direction led to an outgoing merger shock that accelerated electrons, which are detected as relic B. Relic A has a steep and curved spectrum (Fig. 3) indicative of an ageing population of relativistic electrons. We propose that relic A is a candidate adiabatically compressed lobe of a radio galaxy, the compression having been caused by the outgoing shock at B. Simulations of adiabatically compressed cocoons of radio galaxies have shown that, as compression proceeds, the cocoon is torn into filamentary structures, which can appear like a single torus or multiple tori at late stages (Enßlin & Brüggen 2002). The morphology of relic A is complex and filamentary, which compares well with the predicted structures of compressed cocoons. The morphology of B is not smooth at the outer boundary, but shows kink-like features connected to A, implying a possible distortion due to the presence of a radio cocoon in the path of the outgoing shock that led to the formation of A.
Citation: Slee et al. 2001
Citation function: Background; Background
If PTFO 8-8695 is accreting material onto the star from a gas disk devoid of small grains, the excess Hα emission may result entirely from the accreting material whether or not there is a planetary companion present. This accretion-related emission would presumably be similar to Hα emission seen in other CTTSs, many of which also have close companions. If there is a low-mass companion to PTFO 8-8695, accretion from a disk may be through accretion streams such as those proposed by Artymowicz & Lubow (1996) (see also Günther & Kley 2002). At this time, it is not known if a planetary mass companion can excite accretion streams such as those modeled by Artymowicz & Lubow (1996) and Günther & Kley (2002). A few CTTSs binaries are thought to potentially be accreting through accretion streams. These include DQ Tau (Basri et al. 1997; Mathieu et al. 1997), UZ Tau E (Jensen et al. 2007), AK Sco (Alencar et al. 2003), KH 15D (Hamilton et al. 2012), and the eclipsing binary system CoRoT 223992193 in NGC 2264 (Gillen et al. 2014). None of these stars shows the type of Hα variations seen in PTFO 8-8695 where the accretion-related emission appears to move from one side of the line profile to the other as it spirals onto one or both of the stars. This type of line profile behavior is also not seen in the Hα profile variations of single CTTSs in extensive studies of their line profile variability (e.g., Giampapa et al. 1993; Johns & Basri 1995a, 1995b; Johns-Krull & Basri 1997; Oliveira et al. 1998; Alencar et al. 2001), nor is it predicted from theoretical models of magnetospheric accretion such as those shown in Kurosawa & Romanova (2013). While we cannot completely rule out accretion from a tenuous disk as the source of the excess Hα emission observed in PTFO 8-8695, we argue that this is not the most likely explanation of the observed emission. Deep mid-IR or millimeter continuum observations, or a deep search for close circumstellar disk gas emission (e.g., H2 emission, see France et al. 2012), could shed light on whether there is a tenuous disk around this star feeding accretion onto it.
Citation: Mathieu et al. 1997
Citation function: Background; Differences
Using AIA intensity images in six EUV bandpasses, a differential emission measure (DEM) analysis (Cheng et al. 2012; Shen et al. 2015) is performed for the solar flare at 17:03:37 UT, when the oscillations are pronounced. Figure 8 panels b and c plot the DEM profiles in the flaring loop and the background corona, respectively. They contain the same region with an FOV of 3″ × 3″, as enclosed by the red boxes in Fig. 1e. The black profile in each panel is the best-fit DEM solution to the observed fluxes. The colored rectangles represent the errors of the DEM curve, which are calculated from 100 Monte Carlo (MC) realizations of the observational data (Cheng et al. 2012; Tian et al. 2016; Li et al. 2017b). The average temperature (T) and emission measure (EM) inside and outside (background corona) of the flaring loop are also estimated according to their errors, respectively. For example, the confident temperature (log T) range inside the flaring loop is 6.0−7.5, while that outside of the flaring loop is 5.8−7.1, since the temperatures in solar flare are much higher than that in the background corona. Therefore, the number density inside the flaring loop can be estimated with 




n
e


=



E
M
/
w




$ n_e\,{=}\,\sqrt{EM/w} $


 by assuming a filling factor of 1.0 (Tian et al. 2016; Li et al. 2017b), and we can obtain a lower limited density inside the flaring loop of ∼4.7 × 1010 cm−3. On the other hand, the effective LOS depth (



l

≈


H
π
r



∼

4

×


10
10



$ l\,{\approx}\sqrt{H\pi r}\,{\sim}\,4\,{\times}\, 10^{10} $


 cm), instead of the loop width, is applied to calculate the number density outside of the flaring loop (Zhang & Ji 2014; Zucca et al. 2014; Su et al. 2016; Li et al. 2017b), and we get 9.1 × 108 cm−3. Finally, a number density ratio (rd = n0/ne) of ∼0.02 between outside and inside of the flaring loop is determined, which is very close to the density contrast from recent observations (Tian et al. 2016; Li et al. 2017b).
Citation: Tian et al. 2016; Tian et al. 2016
Citation function: Uses; Similarities
Many mechanisms that probably lead to quenching have been proposed, and surely we need some parameters to discriminate between these mechanisms. One of such critical parameters we are looking for is the quenching timescale, which varies from one mechanism to another. Most of these timescales are obtained from simulations (Wright et al. 2019; Wetzel et al. 2013; Walters et al. 2022). Among mass-dependent quenching mechanisms, the supernova feedback is a strong physical process that quickly quenches star formation in ∼0.1 Gyr (Ceverino & Klypin 2009). On the other hand, the AGN feedback timescale is still under debate. Quasar mode AGN feedback is a strong process. Quasars’ lifetimes are not long ( 0.1 Gyr, Hopkins et al. 2006), and the gas outflow driven by quasars can quench star formation at million-year-level timescales (Smethurst et al. 2021). On the other hand, the radio mode of AGN feedback is a slow process (Best et al. 2005); it takes up to and beyond 1 Gyr to establish a balance between cooling and heating to reach a low star formation rate phase (Fabian 2012). In Schawinski et al. (2014) the authors maintain that the timescale of AGN feedback leading to a quenching can be related to the host galaxy type. Furthermore, Hirschmann et al. (2017) simulated the star formation history (SFH) of galaxies with AGN and found that the quenching timescale can have a wide range, from a few hundred Myr to a few Gyr. For environmental quenching mechanisms, strangulation is a long-term process that lasts a few billion years (Peng et al. 2015). Merger-driven quenching does not happen directly after galaxy merger events; a median delay time of 1.5 Gyr is expected, and the timescale varies over a wide range (Rodríguez Montero et al. 2019). Ram pressure stripping is a rapid type of quenching, with a timescale of ∼0.2 Gyr (Steinhauser et al. 2016). Thus, we can use the quenching timescale to determine some of the physical mechanisms involved.
Citation: Hopkins et al. 2006
Citation function: Background