adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020ApJ...896...75S (fulltext)
rays, and calibrate the absolute flux to reduce the raw SAMI observations (Allen et al. 2015 ). Each galaxy field was observed in a set of approximately seven 30 minute exposures that are aligned together by fitting the galaxy position within each hexabundle with a two-dimensional Gaussian and fitting a simple empirical model describing the telescope offset and atmospheric refraction to the centroids. The exposures are then combined to produce a spectral cube with regular 05 spaxels, with a median seeing of 21. More details for Data Release 1 and Data Release 2 reduction can be found in Green et al. ( 2018 ) and Scott et al. ( 2018 ), respectively. 2.2. Sample Selection The GAMA group sample includes all galaxy associations with two or more members, so to ensure the robustness of our group sample, we first select all SAMI observed galaxies that belong to GAMA halos with at least five members to ensure that they have a robustly estimated velocity dispersion (Robotham et al. 2011 ). Moreover, only galaxies classified as cluster members by Owers et al. ( 2017 ) are taken as cluster galaxies. This gives us an initial sample of 574 galaxies (out of a potential 2502 observed by GAMA) in 205 groups in the GAMA regions and 861 galaxies in eight clusters. Stellar population measurements are compromised for 100 galaxies because nearby galaxies or stars affect their observation (van de Sande et al. 2017 ). In addition, we also exclude three more galaxies whose g − i colors suggest contamination from other objects in the field of view and eight galaxies whose annuli do not reflect the shape of their isophotes and/or the central bin is not well aligned with the peak of their flux map (since they could lead to a biased gradient). In this paper, we are particularly interested in whether the evolution of central and satellite galaxies has an environmental dependence. We focus on the passive central galaxies to ensure a like-to-like comparison in determining how their central and satellite status affects their stellar population gradients. We use the SAMI spectroscopic classifications presented in Owers et al. ( 2019 ) to select a homogeneous sample. The galaxies are classified as star-forming, passive, or H δ -strong using the absorption and emission line properties of each SAMI spectrum. We select 843 passive galaxies; of these, 98 are central and 745 are satellite galaxies (Figure 1 ). Figure 1. Distribution of g − i color with stellar mass as a function of galaxy classification for the galaxies included in SAMI v0.11. Unselected passive galaxies are shown as green crosses. Galaxies selected in the final sample are shown as red crosses, and nonpassive galaxies are shown as black crosses. The dotted line is the stellar mass cut at ; galaxies below this mass are excluded from the final sample. To ensure the stellar population measurements we use are reliable, we exclude all galaxies with masses less than , owing to the low signal-to-noise ratio (S/N) and
Paper: 2016MNRAS.455.2592R (fulltext)
while the dark matter distribution in the interior of the halo hardly changes at low redshift (Prada et al. 2006 ; Diemand, Kuhlen Madau 2007 ; Cuesta et al. 2008 ). Recently More, Diemer Kravtsov ( 2015 ) proposed that the best physically based definition of halo radius is the ‘splashback radius’ R sp ≈ 2 R 200m , where there is typically a sharp drop in the density; using this definition, there is actually more halo mass increase than for R vir , R 200m , or R 200c . What is actually relevant to star formation of the central galaxy in the halo is the amount of gas that enters the halo and eventually reaches its central regions. Wetzel Nagai ( 2015 ) have used adaptive refinement tree (ART) hydrodynamic galaxy simulations with a best resolution of about 0.5 kpc to show that infalling gas decouples from dark matter starting at about 2 R 200m and roughly tracks the growth of M 200m . Thus, they argue that pseudo-evolution is relevant to the accretion of dark matter, but not to that of gas. Further evidence that the gas falling into the central regions of haloes roughly tracks the halo MAR is provided by Dekel et al. ( 2013 ), who used a suite of ART hydrodynamic zoom-in galaxy simulations with M vir /10 12 M ⊙ = 0.1 to 2 at z = 2 and best resolution of 35 pc, and found that the gas inflow rate is proportional to the halo MAR, with about half the gas penetrating to 0.1 R vir at redshifts from z = 4 to 2, and with the fraction increasing from z = 2 to 1. Analysis of a subsequent suite of ART hydrodynamic zoom-in galaxy simulations with better resolution and feedback showed that these simulated star-forming galaxies grow stellar mass at the same rate as the specific halo mass increase (Tacchella et al. 2015 ; Zolotov et al. 2015 ). A similar result has been reported in González-Samaniego et al. ( 2014 ) for galaxies formed in haloes of . Star-forming galaxies are known to show a tight dependence of SFR on stellar mass, which is known as the ‘main sequence’ of galaxy formation (Daddi et al. 2007 ; Elbaz et al. 2007 ; Noeske et al. 2007 ; Salim et al. 2007 ), in analogy with the tight dependence on stellar mass of the properties of stars on the stellar main sequence. Dark matter haloes also have an MAR that is roughly proportional to their mass, and we show in Section 2.1 of this paper that the dispersion of the halo MAR at a given halo mass is 0.3 to 0.4 dex, similar to the dispersion of the SFR on the main sequence. It was this equality that originally motivates us to examine more closely the connection between mass accretion and star formation. Our analysis is limited to the connection between distinct haloes and central galaxies only. One reason is that subhaloes lose mass via tidal stripping, resulting in negative values of accretion rates (see e.g. van den Bosch, Tormen Giocoli 2005 ). Satellite galaxies are also affected in other ways by their proximity to central galaxies. Therefore, studying the connection between subhalo mass accretion
Paper: 2019A&A...629A.136S (fulltext)
in our model to reproduce the free–free continuum spectrum flattening near 2 mm and the surface brightness depression at the center in the continuum and mm-RRL maps. We estimate average electron temperatures of T e ~ 8000–11 000 K and densities in the range n e ≈ 10 7 –10 6 cm −3 in the ionized core of MWC 922. A density radial law n e ∝ r −2.7 , and a smooth variation with the latitude (decreasing from the disk outer boundary to the poles) is inferred. The density radial law could be slightly shallower than adopted, as suggested by the H39 α cubes, which show that the emission from this transition arises at slightly larger distances from the center than predicted by our model. We deduce a mass-loss rate of the fast bipolar wind of Ṁ fast ~ few × 10 −6 M ⊙ yr −1 . Assuming that a slow wind is also present at intermediate latitudes (i.e., bridging the ionized surface of the rotating disk and the fast bipolar wind), we obtain a mass-loss rate of Ṁ slow ~ few × 10 −7 M ⊙ yr −1 . The dynamical age of the winds traced by our observations is a few decades at most, with the innermost layers of the fast wind (at ~29–51 au) ejected just ~1.5–2.6 yr ago. The total ionized mass deduced from the free–free continuum emission at millimeter wavelengths is M H II ~ 2 × M ⊙ . The dominant mass component in the close environment of MWC 922 is probably that probed by the ~160 K dust millimeter continuum emission, from which we deduce ~ 8 M ⊙ . In Sect. 5 we discuss the evolutionary status of MWC 922 from different perspectives and provide a number of arguments that favor a post-main sequence stage. We believe that MWC 922 could be a ~15 M ⊙ blue giant (with T eff ~ 20–30 kK and L ⋆ ~ 5.9 × 10 4 L ⊙ ) located at d ~ 3 kpc that has recently left the main sequence (~10 Myr ago) and is probably undergoing a phase of mass exchange with an unseen companion. Appendix A Additional figures The Band 3and Band 6 continuum emission maps of MWC 922 obtained with 15 and 30 mas-resolution, respectively,are shown in Fig. A.1 . We note the X-shaped morphology, reminiscent of that of the near-IR andmid-IR nebulosities around MWC 922 (Sect. 1 ). The predictions from our model, which is described in Sect. 4 and is schematically represented in Fig. 6 , are shown in Fig. A.2 with the same angular resolution. The equatorial plane of the disk is oriented along PA ~ 45°. The velocity-channel maps of the H30 α and H39 α lines towards MWC 922 are presented in Figs. A.3 and A.4 , respectively. In Fig. A.5 a summary of the observational results for the weak non- α mm-RRLs detected in MWC 922 together with our model predictions is offered. In Figs. A.6 and A.7 we summarize the comparison between data and model for the H30 α and H39 α transitions with super-resolution (15 and 30 mas, respectively). For H30 α , we also display the predictions given by a model where rotation has been suppressed in the fast wind to show that when there is no rotation of the fast wind, the peaks of the red- and
Paper: 2019ApJ...874...26S (fulltext)
on the Reynolds number. We select the dependence appropriate for spherical grains. For better comparison of the coupling between dust particles with a certain size and the disk gas, the dimensionless Stokes number is defined asParticles with Stokes number of unity come to match the gas velocity in one local orbital period. For a wide range of gas densities, submicrometer dust grains have and hence come quickly to rest in the gas reference frame. The dust scale height ( h d ) in a turbulent protoplanetary nebula can be calculated following Dubrulle et al. ( 1995 ) and Youdin Lithwick ( 2007 ):For tightly coupled particles with , h d ≈ h g , and we approximate the dust scale height by that of the gas at t = 0. To model the dust size distribution at t = 0, we adopt a grain-size distribution with an MRN (Mathis et al. 1977 ) power-law index, , where N ( a ) da is the number of dust particles of radii between . We consider that dust grains already grow beyond the ISM size in the molecular cloud phase (Suttner Yorke 2001 ) and adopt the maximum and minimum sizes of the dust size distribution at t = 0 as a max = 1.0 μ m and a min = 0.1 μ m. To begin our grain growth and settling simulation, we make two assumptions: 1. Gas and dust of all sizes are dynamically coupled and well mixed at t = 0, with (this will not be true at later times); 2. The dust to gas mass ratio is η = 0.01, similar to the ISM. 3. Dust Evolution Model The main objective of this work is to obtain the disk temperature profile, which is controlled by the dust opacity. To simulate dust evolution, we introduce a hybrid model in which the collisional dust growth is implemented through a weighted Monte Carlo method along with a Lagrangian Monte Carlo prescription for vertical settling and diffusion. 3.1. Collision Model The outcome of a modeled collision between two dust particles in a protoplanetary environment has many possibilities according to laboratory experiments. On the experimental side, Güttler et al. ( 2010 ) presented 19 possible collisional outcomes for particles with various mass ratios, speeds, and porosities. However, it is prohibitively computationally expensive to include all possibilities in a global disk model. For simplicity, we adopt a collisional model that includes only sticking and fragmentation. We treat collisions as a binary process, identifying the smaller mass as the projectile and the bigger mass as the target . The collision outcome is determined by the relative velocity (see Section 3.2 for a description of our velocity computation). If the particles collide with a velocity less than a threshold velocity v frag , they stick and form a new particle with mass . When , the collision results in fragmentation. As particles grow by sticking, their eddy-crossing times drop, leading to lower coupling with the gas and higher collision speeds (Ormel Cuzzi 2007 ). When the collision speed reaches v frag , instead of sticking, both particles fragment. For such an
Paper: 2019A&A...629A..90H (fulltext)
of solids, α tur . The previous work assumed α acc = α tur . We will show that Drift-BKR and Diff-BKR of silicate grains are the most essential factor for the occurrence of the runaway pile-up of silicate grains and that of icy pebbles is also resulted in by Diff-BKR. The ratio between α tur and α acc controls relative importance between the runaway pile-up of silicate grains inside the snow line and that of icy pebbles outside the snow line. We map the parameter regions of F p/g and α tur ∕ α acc for GI (runaway pile-up) of silicate grains, GI of icy pebbles and SI of the icy pebbles. In Sect. 2 , we describe our basic set-ups of numerical appro- aches. In Sect. 3 , we present our numerical results. In Sect. 4 , we discuss the results. In Sect. 5 , we summarize our paper. 2 Numerical methods In this section, we discuss our numerical methods and models. 2.1 Gas disk model In this paper, we use classical α -accretion disk model ( Shakura Sunyaev 1973 ; Lynden-Bell Pringle 1974 ) where the surface density of the gas is expressed as a function of the gas accretion rate Ṁ gas and the effective viscosity ν acc as and ν acc is written using the sound speed c s , the Keplerian orbital frequency Ω and the dimensionless effective viscous parameter α acc given as The above equation assumes a steady state of the background gas. When the gas accretion is dominated by the viscous diffusion, α acc equals the dimensionless turbulence parameter α tur . In contrast, when the gas accretion is regulated by the disk wind-driven accretion ( Bai et al. 2016 ; Suzuki et al. 2016 ), generally α acc is larger than α tur ( Armitage et al. 2013 ; Hasegawa et al. 2017 ). The isothermal sound speed of the gas is written as where k B is the Boltzmann constant, μ gas is the molecular weight of the gas and T is the temperature of the gas. In this paper, the temperature profile is fixed as We note that our disk is somewhat a hot disk but we chose this profile so that we can directly compare with the previous work ( Schoonenberg Ormel 2017 ). Under the above conditions, the gas scale height is written as The midplane gas density is . In this paper, we assume that the above gas profile is unchanged through the simulation and thus μ gas = 2.34 m proton , where m proton is the proton mass. The accretion velocity of the gas v gas is written as where a negative value indicates radial drift to the central star. 2.2 Radial motions of solids and vapor In this work, we describe a pebble as the mixture of many micron-sized silicate grains covered by water ice that is also used in the previous paper (see “many-seeds pebble model” in Schoonenberg Ormel 2017 and the models used by Saito Sirono 2011 and Ida Guillot 2016 ). In our one-dimensional calculation, we adopt the single-size approximation ( Ormel 2014 ; Sato et al. 2016 ) where the pebble size is represented by a single size depending on the radial distance r , and solve the system integrated over the vertical direction of the disk
Paper: 2020ApJ...898..156W (fulltext)
ocean with a depth of 50 m everywhere; no continent is considered. Sea ice is allowed to form when the surface temperature is below the freezing point (271.35 K), and the albedos of sea ice and snow depend on the stellar spectrum. For the visible band (0.7 μ m), the snow albedo is 0.80 and the ice albedo is 0.67. For the near-infrared band (>0.7 μ m), it is 0.68 for snow and 0.30 for sea ice. Neither oceanic dynamics nor sea ice dynamics are included in the simulations. By default, the time step of the experiments is 30 minutes, except for the f05_L26, f19_L51, and f19_L74 experiments, within which the time step is 15 minutes to avoid numerical instability. Each experiment was run for tens of Earth years until the sea ice and surface temperature reach equilibrium and the global-mean net energy imbalance at the top of the atmosphere (TOA) is within 1.0 W m −2 . Averages of the last 5 or 10 Earth years were used here. 3. Results 3.1. Effect of Varying the Horizontal Resolution Table 1 summarizes the global-mean characteristics of the slow rotation experiments. Overall, varying the horizontal resolution has a small effect on the surface temperature (Figures 1 (a)–(c)). In global mean, the surface temperature in the experiment of f45_L26 is 3.7 K higher than that of f19_L26 and 3.2 K higher than that of f05_L26. In the rapid rotation experiments, the difference is also small (Figure 2 ): the surface temperature difference between f45_L26 and f05_L26 is 1.0 K in global mean. In the following analyses, we focus on the results of slow rotation experiments, because the underlying mechanisms are the same. Figure 1. Surface temperature (K) in five experiments with resolutions of f45_L26, f19_L26, f05_L26, f19_L51, and f19_L74 (see Section 2 for detailed descriptions). In all these experiments, the stellar flux is 1800 W m −2 , star temperature is 3700 K, and rotation period (=orbital period) is 38.66 Earth days. Figure 2. Surface temperature in the rapid rotation experiments with two resolutions of f45_L26 (a) and f05_L26 (b). In these two experiments, the stellar flux is 1300 W m −2 , the star temperature is 2600 K, and the rotation period (=orbital period) is 4.25 Earth days. The global-mean surface temperature is 294.7 K in (a) and 293.7 K in (b). Table 1 Global-mean Characteristics of the Simulated Climate of a Slow Rotation Aquaplanet Under Five Different Resolutions in the Model ExoCAM Note. For the definition of total relative humidity, please see the text. The clear-sky greenhouse effect is defined as the difference in upward thermal infrared flux between the surface and the top of the model. For planetary albedo, the difference is also small (Figures 3 (a1)–(c1)). The f45_L26 experiment has a planetary albedo of 0.440, being 0.005 higher than that of f19_L26 and 0.006 higher than that of f05_L26, which corresponds to a difference in global-mean shortwave reflection of 2.25 and 2.70 W m −2 , respectively. The cloud longwave radiation effects
Paper: 2020ApJ...905..101T (fulltext)
have been used for the calculations of lifetimes of excited levels. These lifetimes have been included in Table 1 . There are a number of even-parity metastable levels that can decay to lower levels only via forbidden electric quadrupole (E2) and magnetic dipole (M1) transitions. The most extensive calculation of radiative transition probabilities and lifetimes for the metastable levels of Ti II has been presented by Deb et al. ( 2009 ). Comparison of their lifetimes with the present results is given in Table 3 . We note a very reasonable agreement between the two calculations with standard deviation of about 20%. However, there are a few levels with considerable disagreement. For example, the lifetimes of the levels of Deb et al. ( 2009 ) exceed our values by five times. For the state we found extremely strong, almost half and half, mixing with the 3 d 2 4 s 2 P state. It makes even the configuration assignment for these levels somewhat ambiguous. The matrix elements for the levels with such strong mixing are very sensitive to the details of calculation and it may be the reason for a large disagreement with the CIV3 calculation in this case. Another example of large discrepancies is the levels which also show strong mixing with the 3 d 3 configuration levels. In this case, the lifetimes from the CIV3 calculation are two times smaller than our values. Table 2 Present Calculated Line Strengths ( S ), Oscillator Strengths ( f ik ), and Transition Probabilities ( A ki (s −1 )) for E1, E2, and M1 Transitions in Ti II Together with Their Wavelengths λ ( Å ) (This table is available in its entirety in machine-readable form.) Only a portion of this table is shown here to demonstrate its form and content. A machine-readable version of the full table is available. Table 3 Comparison of the Calculated and Experimental Lifetimes (ns) for the Metastable States in Ti II The comparison of the present calculated lifetimes with the measurements of Hartman et al. ( 2003 , 2005 ) and Palmeri et al. ( 2008 ) has been presented in Table 3 for seven levels belonging to the 3 d 3 , 3 d 2 4 s , and 3 d 4 s 2 configurations. We have also included available theoretical results of Bautista et al. ( 2006 ) and Deb et al. ( 2009 ) in Table 3 . Our calculated lifetimes closely agree with the measurement of Palmeri et al. within the experimental uncertainties. However, the differences with the measurements of Hartman et al. ( 2003 ) are up to a factor of 2. It indicates the importance of correction due to the repopulation effects that was incorporated in the measurement of Palmeri et al. ( 2008 ). It may also be the reason of discrepancies between the previous theoretical results of Deb et al. ( 2009 ) and Bautista et al. ( 2006 ) and experimental results of Hartman et al. ( 2003 , 2005 ). Good agreement of the present results with measured lifetimes of Palmeri et al. ( 2008 ) confirms that our target wave function expansions adequately include main correlation
Paper: 2016MNRAS.456.1451T (fulltext)
that the crucial difference between both models is the much higher proton fraction in the BHF approach. In this case, the DU threshold is already reached at ρ = 0.44 fm −3 ( x p = 0.136), whereas with the APR it is delayed to ρ = 0.82 fm −3 ( x p = 0.140). Figure 1. Pressure and energy density (a), and proton and muon fractions (b) as functions of baryon number density ρ in beta-stable matter for the APR and BHF EOS. The lower panels show NS mass (d) and radius (c) as functions of the central density ρ c . The DU onset is indicated by vertical dotted lines. On the contrary, the APR EOS is somewhat stiffer, i.e. features a larger pressure and energy density. We also mention that the APR EOS becomes superluminal at ρ = 0.85 fm −3 , whereas BHF remains always below the critical threshold (Burgio Schulze 2010 ; Taranto et al. 2013 ). By solving the standard Tolman–Oppenheimer–Volkov equations for the NS structure, this input yields the NS (mass,radius) – central density relations shown in panels (c) and (d) of Fig. 1 . We remark that both models reach maximum masses (slightly) above two solar masses and predict very similar radii in spite of their different matter composition. With BHF the DU process is active in nearly all stars, M /M ⊙ > 1.10, while with APR only in the most heavy ones, M /M ⊙ > 2.03. This has profound consequences for the cooling behaviour. 3 PAIRING GAPS AND CRITICAL TEMPERATURES Of vital importance for any cooling simulation is the knowledge of the 1S0 and 3PF2 pairing gaps for neutrons and protons in beta-stable matter, which on one hand block the DU and MU reactions, and on the other hand open new cooling channels by the PBF mechanism for stellar matter in the vicinity of the critical temperature (Yakovlev et al. 2001 ). As usual, we focus in this work on the most important proton 1S0 (p1S0) and neutron 3PF2 (n3P2) pairing channels and neglect the less important remaining combinations. As stressed before, the gaps should be computed in a framework that is consistent with the determination of the EOS, i.e. be based on the same NN interaction and using the same medium effects (TBF and effective masses), and indeed we follow this procedure here, by using the results of Zhou et al. ( 2004 ), which employed the same V18+UIX nuclear interaction and BHF SP spectra for the calculation of the gaps. To be more precise, and focusing on the more general case of pairing in the coupled 3PF2 channel, the pairing gaps were computed on the BCS level by solving the (angle-averaged) gap equation (Amundsen Østgaard 1985 ; Baldo et al. 1992 , 1998 ; Takatsuka Tamagaki 1993 ; Elgarøy et al. 1996 ; Khodel, Khodel Clark 1998 ) for the two-component L = 1, 3 gap function, with while fixing the (neutron or proton) density, Here e ( k ) are the BHF SP energies, equation ( 2 ), containing contributions due to two-body and three-body forces, μ ≈ e ( k F ) is the (neutron) chemical potential determined self-consistently from equations ( 6 )–( 8 ), and