adsabs/AstroCLIMB huggingface dataset:
Paper: How Do the Velocity Anisotropies of Halo Stars, Dark Matter, and Satellite Galaxies Depend on Host Halo Properties?
Authors: Jiaxin He, Wenting Wang, Zhaozhou Li, Jiaxin Han, Vicente Rodriguez-Gomez, Donghai Zhao, Xianguang Meng, Yipeng Jing, Shi Shao, Rui Shi, Zhenlin Tan
Figure caption: Velocity anisotropy profiles of dark matter particles from TNG300. The curves with the same color are based on systems in the same mass range of ﹩{\mathrm{log}}_{10}{{M}}_{200}/{{M}}_{\odot }﹩, as indicated by the first few numbers in the legend. We further divide them into three different bins of halo concentration (c 200), also indicated by the legend. The error bars are the 1σ scatters of 100 bootstrapped subsamples of galaxy systems in the same mass bin.
Paper: How Do the Velocity Anisotropies of Halo Stars, Dark Matter, and Satellite Galaxies Depend on Host Halo Properties?
Authors: Jiaxin He, Wenting Wang, Zhaozhou Li, Jiaxin Han, Vicente Rodriguez-Gomez, Donghai Zhao, Xianguang Meng, Yipeng Jing, Shi Shao, Rui Shi, Zhenlin Tan
Figure caption: Similar to Figure 8, but based on halo stars.
Paper: How Do the Velocity Anisotropies of Halo Stars, Dark Matter, and Satellite Galaxies Depend on Host Halo Properties?
Authors: Jiaxin He, Wenting Wang, Zhaozhou Li, Jiaxin Han, Vicente Rodriguez-Gomez, Donghai Zhao, Xianguang Meng, Yipeng Jing, Shi Shao, Rui Shi, Zhenlin Tan
Figure caption: A joint fit to the velocity anisotropy differences between halo stars and dark matter particles (black solid line), using results from TNG300, TNG100, and TNG50 together. Here, the velocity anisotropies of halo stars and dark matter particles are calculated based on particles with galactocentric radii of r  s  < r < 3 r  s .
Paper: Predicting Arrival Times of the CCMC CME/Shock Events Based on the SPM3 Model
Authors: Yidan Liang, 一丹 梁, Xinhua Zhao, 新华 赵, Nanbin Xiang, 南彬 向, Shiwei Feng, 士伟 冯, Fuyu Li, 富羽 李, Linhua Deng, 林华 邓, Miao Wan, 苗 万, Ran Li, 冉 李
Figure caption: The percentage distribution of the events number along the longitude (latitude). The black line represents the percentage of type A and the blue line represents the percentage of type B events.
Paper: Predicting Arrival Times of the CCMC CME/Shock Events Based on the SPM3 Model
Authors: Yidan Liang, 一丹 梁, Xinhua Zhao, 新华 赵, Nanbin Xiang, 南彬 向, Shiwei Feng, 士伟 冯, Fuyu Li, 富羽 李, Linhua Deng, 林华 邓, Miao Wan, 苗 万, Ran Li, 冉 李
Figure caption: The percentage distribution of the event number along the ratio of CME speed to solar wind speed (the CME’s speed toward the Earth’s component). The black line represents the percentage of type A events and the blue line represents the percentage of type B events.
Paper: Predicting Arrival Times of the CCMC CME/Shock Events Based on the SPM3 Model
Authors: Yidan Liang, 一丹 梁, Xinhua Zhao, 新华 赵, Nanbin Xiang, 南彬 向, Shiwei Feng, 士伟 冯, Fuyu Li, 富羽 李, Linhua Deng, 林华 邓, Miao Wan, 苗 万, Ran Li, 冉 李
Figure caption: The distribution of forecast errors for the 127 successfully predicted events plotted versus the CME initial speed. (The 127 events are the SPM3(CDAW) events mentioned below.)
Paper: Predicting Arrival Times of the CCMC CME/Shock Events Based on the SPM3 Model
Authors: Yidan Liang, 一丹 梁, Xinhua Zhao, 新华 赵, Nanbin Xiang, 南彬 向, Shiwei Feng, 士伟 冯, Fuyu Li, 富羽 李, Linhua Deng, 林华 邓, Miao Wan, 苗 万, Ran Li, 冉 李
Figure caption: The top panel (bottom panel) shows a scatterplot of the shock's transit time and the initial speed of the CME (the background solar wind speed) for type A events. The black diamonds represent the observation data, and the blue dashed line represents the linear fitting line with the CC of −0.62(−0.05).
Paper: Predicting Arrival Times of the CCMC CME/Shock Events Based on the SPM3 Model
Authors: Yidan Liang, 一丹 梁, Xinhua Zhao, 新华 赵, Nanbin Xiang, 南彬 向, Shiwei Feng, 士伟 冯, Fuyu Li, 富羽 李, Linhua Deng, 林华 邓, Miao Wan, 苗 万, Ran Li, 冉 李
Figure caption: The variations of the annual SSN and the annual mean absolute error of the SPM3 model.
Paper: Predicting Arrival Times of the CCMC CME/Shock Events Based on the SPM3 Model
Authors: Yidan Liang, 一丹 梁, Xinhua Zhao, 新华 赵, Nanbin Xiang, 南彬 向, Shiwei Feng, 士伟 冯, Fuyu Li, 富羽 李, Linhua Deng, 林华 邓, Miao Wan, 苗 万, Ran Li, 冉 李
Figure caption: The percentage distribution of the events number along the angular width of CME (AWCME). The black line represents the percentage of type A, and the blue line represents the percentage of type B.
Paper: Predicting Arrival Times of the CCMC CME/Shock Events Based on the SPM3 Model
Authors: Yidan Liang, 一丹 梁, Xinhua Zhao, 新华 赵, Nanbin Xiang, 南彬 向, Shiwei Feng, 士伟 冯, Fuyu Li, 富羽 李, Linhua Deng, 林华 邓, Miao Wan, 苗 万, Ran Li, 冉 李
Figure caption: The mean absolute errors of predicting the shock arrival time of different models for these 23 events.
Paper: Predicting Arrival Times of the CCMC CME/Shock Events Based on the SPM3 Model
Authors: Yidan Liang, 一丹 梁, Xinhua Zhao, 新华 赵, Nanbin Xiang, 南彬 向, Shiwei Feng, 士伟 冯, Fuyu Li, 富羽 李, Linhua Deng, 林华 邓, Miao Wan, 苗 万, Ran Li, 冉 李
Figure caption: Yearly mean SSN distribution (1996–2023) (Source: WDC-SILSO, Royal Observatory of Belgium, Brussels).
Paper: Impacting Atmospheres: How Late-stage Pollution Alters Exoplanet Composition
Authors: Emilia Vlahos, Yayaati Chachan, Vincent Savignac, Eve J. Lee
Figure caption: Atmospheric mass fraction of refractory carbons (top) and water (middle) with the corresponding atmospheric carbon-to-oxygen mole ratio (bottom). Mass fractions are calculated for a solid disk consisting of 17.7% refractory carbon and 82.3% water ice. We assume atmospheric gas is initially of solar composition (C/O ∼ 0.48; N. Grevesse & A. Noels 1993) and track how oxygen and carbon accreted in the form of H2O and refractory carbons alter the C/O ratio. In the leftmost panels, we show the C/O ratios of a planet migrating inwards from 3.5 au in a disk with f gas = 1 and f gas = 10−2. In the rightmost panels, we show the C/O ratios of a planet accreting in situ at 3.5 au in a disk f gas = 10−2 and f gas = 10−4. The vertical lines indicate the advective-radiative boundary, and all profiles terminate at the radiative-convective boundary. The maximum level of C/O ratio under pollution (i.e., the solid state C/O in the disk ∼0.24 representing the case where carbon refractory elements ablate just as easily as H2O) is indicated in dashed line. We draw the disk gas C/O ratio with a horizontal bar from 1× to 3× the solar value, with the latter representing the expected disk gas C/O ratio beyond the water but inside the CO2 ice line (see text for detail). The bump in C/O ratio for f solid = 10−4 in the second and fourth panels (bottom row) arises as the atmosphere becomes locally saturated with water.
Paper: Impacting Atmospheres: How Late-stage Pollution Alters Exoplanet Composition
Authors: Emilia Vlahos, Yayaati Chachan, Vincent Savignac, Eve J. Lee
Figure caption: Mass fractions of the accreted SiO2 (at 0.1 au, top) and H2O (at 3.5 au, bottom) relative to the total gas (including the evaporated mass) after an accretion timescale of 2 Myr. Solid lines with color represent the accretion of 10 m impactors, and dashed lines with color represent the accretion of 1 km impactors. Different colors represent variations in solid disk density and the associated mass-accretion rate at a given orbital distance. SiO2 mass fraction is limited by saturation (black solid line), causing overlap between curves. By contrast, H2O mass fraction is limited to unity in the case where the mass of deposited H2O exceeds that of the initial gas. The left panels depict envelopes in gaseous disk densities of 10−2 MMEN, and the right panels depict envelopes in gaseous disk densities of 10−4 MMEN. All profiles are for dusty opacities. We include solar silicon and oxygen abundances consistent with Z = 0.02 and N. Grevesse & A. Noels (1993) for comparison.
Paper: Impacting Atmospheres: How Late-stage Pollution Alters Exoplanet Composition
Authors: Emilia Vlahos, Yayaati Chachan, Vincent Savignac, Eve J. Lee
Figure caption: We compare the radial profile of deposited mass of impactors (left and middle columns) and the final metallicity (deposited mass/gas mass only in H and He; right column) in atmospheres of varying initial metallicity Z. The left and middle panels show the mass deposited by a 10 m impactor as a function of the planet’s radius and atmospheric pressure, respectively. The upper panels depict SiO2 at 0.1 au and the lower panels depict H2O at 3.5 au. All atmospheric profiles have f gas = 10−2 and f solid = 10−5. Pentacles indicate the advective-radiative boundary.
Paper: Impacting Atmospheres: How Late-stage Pollution Alters Exoplanet Composition
Authors: Emilia Vlahos, Yayaati Chachan, Vincent Savignac, Eve J. Lee
Figure caption: The left panels show the infall velocity (top) and atmospheric sound speed (bottom) for impactors of 10 m (solid) and 1000 m (dashed) and at 0.1 au (blue) and 3.5 au (black). The x-axis represents the distance to the planet’s center, normalized with respect to the outer radius (r/R out). The right panel shows the drag regimes of these four cases as characterized by their Mach/Reynolds numbers. The corresponding drag coefficient (Equation (11)) is shown in the contour plot. In the Epstein regime (Ma ≳ Re; left edge), the drag coefficient depends on the ratio between the impactor surface temperature and the gas temperature. Here, we consider the case T s = T g. All atmospheric profiles have dusty opacity and gas disk densities of 0.01 MMEN.
Paper: Impacting Atmospheres: How Late-stage Pollution Alters Exoplanet Composition
Authors: Emilia Vlahos, Yayaati Chachan, Vincent Savignac, Eve J. Lee
Figure caption: Mass fractions of accreted H2O relative to the total gas for planets migrating inwards from 3.5 au in disks of varying gaseous surface density (f gas = 1 at the top, f gas = 10−2 at the bottom). Rapid migration in gas-heavy disks leads to a significantly lower rate of pollution. All profiles are for dusty opacities. We indicate in horizontal dotted line the oxygen mass fraction consistent with Z = 0.02 and N. Grevesse & A. Noels (1993).
Paper: Dynamical Origin of the Vertical Metallicity Gradient of the Milky Way Bulge
Authors: Bin-Hui Chen, Juntai Shen, Zhong Liu
Figure caption: The face-on and side-on views of the normalized logarithmic surface densities for the major Galactic components defined by M. Ness et al. (2013a) for the three models. From top to bottom: the single-disk model, the 3DR model, and the 3DG model. From left to right: Component A ([Fe/H] > 0.0), Component B (−0.5 < [Fe/H] ≤ 0.0), and Component C (−1.0 < [Fe/H] ≤ −0.5). In the single-disk and 3DR models, the bar structure (face-on) and the BPX structure (side-on) are more pronounced in the more metal-poor components. In contrast, these structures are more pronounced in the more metal-rich components in the 3DG model.
Paper: Impacting Atmospheres: How Late-stage Pollution Alters Exoplanet Composition
Authors: Emilia Vlahos, Yayaati Chachan, Vincent Savignac, Eve J. Lee
Figure caption: Left: radial profiles of power terms that contribute to the thermal evolution of the impactor, as well as the net power (always zero according to Equation (29)). Right: the surface temperature of the impactor as well as the pre- and post-shock temperatures of the gas over the impactor trajectory. Impactor size and orbital distance is consistent between left and right panels for a given row. All atmospheric profiles have dusty opacity and f gas = 10−2.
Paper: The SAGA Survey. V. Modeling Satellite Systems around Milky Way–Mass Galaxies with Updated UNIVERSEMACHINE 
Authors: Yunchong Wang, Ethan O. Nadler, Yao-Yuan Mao, Risa H. Wechsler, Tom Abel, Peter Behroozi, Marla Geha, Yasmeen Asali, Mithi A. C. de los Reyes, Erin Kado-Fong, Nitya Kallivayalil, Erik J. Tollerud, Benjamin Weiner, John F. Wu
Figure caption: Best-fit UM-SAGA model compared to observational constraints at z ∼ 0. All SAGA results in this panel have been converted to a Chabrier IMF. Solid curves in each panel are the predictions from the best-fit UM-SAGA model in the same mass or radial bins as the corresponding observational constraints. The shaded regions show the 68% (dark) and 95% model posteriors of the mean observables sampling 800 points from the posteriors. The vertical gray shaded regions represent model extrapolations to M ⋆ = 106.5 M ⊙. Top left: quenched fractions vs. galaxy stellar mass, f  Q (M ⋆), for SAGA-like satellites (green), SDSS-like isolated field galaxies (dark blue), and all galaxies (black). Dashed curves of the same color are model extrapolations of f  Q  for each galaxy sample outside the mass range where they have observational constraints. The dotted curves are UM DR1 predictions of the three galaxy samples, which significantly underestimate f  Q  at M ⋆ ≲ 108.5 M ⊙. The leftmost black data point from A. E. Bauer et al. (2013) represents the minimum mass to which UM DR1 was constrained in f  Q . The shaded posteriors around the black curve are negligible, highlighting the nonchanging high-mass f  Q (v Mpeak) part inherited from UM DR1. Top right: quenched fraction as a function of 2D projected distance to the host. Bottom left: average specific SFR as a function of stellar mass. Light blue points are NUV sSFRs of SAGA star-forming satellites (SFing for short), and the light blue solid curve is the UM-SAGA prediction (posterior in blue shading). The black points are GALEX NUV sSFR (S. Salim et al. 2007) for all environments, and the black dashed curve is the corresponding UM-SAGA prediction. The dotted–dashed line is the extrapolated linear fit to SDSS Hα SFRs in various environments (Y.-j. Peng et al. 2010). Bottom right: SAGA3 satellite SMF​​​​​ ﹩{\phi }_{\mathrm{SAGA}}^{\star }﹩ combining satellites from all 101 hosts.
Paper: The SAGA Survey. V. Modeling Satellite Systems around Milky Way–Mass Galaxies with Updated UNIVERSEMACHINE 
Authors: Yunchong Wang, Ethan O. Nadler, Yao-Yuan Mao, Risa H. Wechsler, Tom Abel, Peter Behroozi, Marla Geha, Yasmeen Asali, Mithi A. C. de los Reyes, Erin Kado-Fong, Nitya Kallivayalil, Erik J. Tollerud, Benjamin Weiner, John F. Wu
Figure caption: Schematic diagram of our modeling approach. The upper half highlights data constraints for UM-SAGA, consisting of newly added low-mass galaxy data (Section 2) and all other observations used in UM DR1. The lower half highlights model modifications to UM DR1 (Section 3) and how we select mock galaxy catalogs from the simulation (Section 4). SMF: stellar mass function; w  p : projected two-point correlation function; r  p : projected distance.
Paper: The SAGA Survey. V. Modeling Satellite Systems around Milky Way–Mass Galaxies with Updated UNIVERSEMACHINE 
Authors: Yunchong Wang, Ethan O. Nadler, Yao-Yuan Mao, Risa H. Wechsler, Tom Abel, Peter Behroozi, Marla Geha, Yasmeen Asali, Mithi A. C. de los Reyes, Erin Kado-Fong, Nitya Kallivayalil, Erik J. Tollerud, Benjamin Weiner, John F. Wu
Figure caption: Distribution of stellar mass vs. halo mass (upper panel) and K-band magnitude (lower panel) of 2500 mock SAGA-like hosts in c125-2048 (gray contours; 10% intervals) that well reproduce the distributions of the 101 observed SAGA hosts (green stars). The stellar mass values for the SAGA hosts are converted from their observed K-band magnitude (Paper III); their halo masses are group masses cross-matched to the 2MRS catalog (S. H. Lim et al. 2017). The MW’s observationally inferred stellar and halo masses are from M. Cautun et al. (2020). We convert the M 200 halo masses for the MW to Δ c (z = 0) = 99.2 virial masses using their observationally inferred halo concentration. We quote the UM-SAGA M ⋆ values for these SAGA-like hosts since the model updates in the low-mass galaxy regime do not affect the galaxy–halo connection at the MW-mass scale (Figure 4). The M  K  values for the simulated SAGA-like hosts are obtained by abundance matching the 6dF luminosity function (D. H. Jones et al. 2006) to halo peak maximum circular velocity in c125-2048.
Paper: On the Formation of Super-Alfvénic Flows Downstream of Collisionless Shocks
Authors: Adnane Osmane, Savvas Raptis
Figure caption: Surfaces for the temperature anisotropy T ⊥/T ∥ > 0 as a function of θ  Bn  and M  A  for a density compression ratio r = ρ 2/ρ 1 = 2 in the left panel, and r = 2.6 in the right panel. Values of the anisotropy for which the second law of thermodynamics is violated are not plotted. The surfaces indicates shock parameters for which the firehose instability can become unstable.
Paper: On the Formation of Super-Alfvénic Flows Downstream of Collisionless Shocks
Authors: Adnane Osmane, Savvas Raptis
Figure caption: MMS observations of quasi-parallel magnetosheath jets close to the bow shock using high-resolution burst measurement as shown in the temperature anisotropy (T ⊥/T ∥) vs. ﹩{\mathrm{log}}_{2}({\beta }_{\parallel })﹩ scatter plot. Instability thresholds for oblique firehose instability (lower red dotted line) and the mirror instability threshold (top dotted black line) are shown (S. D. Bale et al. 2009). Each mark represents one data point of 0.15 s resolution. The nontransparent points are below the firehose-instability threshold (∼11%).
Paper: On the Formation of Super-Alfvénic Flows Downstream of Collisionless Shocks
Authors: Adnane Osmane, Savvas Raptis
Figure caption: Evolution of the perturbed flow associated with a firehose instability for three initial pressure anisotropies Δ(0) = [−0.1, −0.3, −0.5]. The time is normalized by the Larmor frequency. It takes t ≃ 100/Ω ≃ 16 Larmor periods T = 2π/Ω for the flow to become comparable to the background flow.
Paper: On the Formation of Super-Alfvénic Flows Downstream of Collisionless Shocks
Authors: Adnane Osmane, Savvas Raptis
Figure caption: Kinetic energy density normalized by the magnetic field energy density as a function of normalized time τ = ∣U  b ∣t/L 0 for an expanding flow. Curves are plotted for β ∥(0) varying between 2 and 12. Larger kinetic energy densities are reached for higher β ∥ values. This figure indicates that high plasma beta rarefied flows can become super-Alfvénic, with values of the order of 2–3 times the Alfvén speed.
Paper: On the Formation of Super-Alfvénic Flows Downstream of Collisionless Shocks
Authors: Adnane Osmane, Savvas Raptis
Figure caption: Firehose instability criteria as a function of θ  Bn  and parameterized for Mach number values (M  A ) ranging between 3 and 15. The left panel is for a density compression ratio of r = ρ 2/ρ 1 = 2, and the right panel is for r = 2.6.
Paper: The SAGA Survey. V. Modeling Satellite Systems around Milky Way–Mass Galaxies with Updated UNIVERSEMACHINE 
Authors: Yunchong Wang, Ethan O. Nadler, Yao-Yuan Mao, Risa H. Wechsler, Tom Abel, Peter Behroozi, Marla Geha, Yasmeen Asali, Mithi A. C. de los Reyes, Erin Kado-Fong, Nitya Kallivayalil, Erik J. Tollerud, Benjamin Weiner, John F. Wu
Figure caption: Exploring the effect on satellite and isolated field f  Q  due to different correlation strengths between halo growth/stripping and star formation/quenching (r  c ). In these four panels, we only vary the 4 r  c  parameters (insets) defined in Equation (6) and keep the other 11 parameters we explored (including the 6 in the f  Q (v Mpeak) relation; Equation (2)) fixed to their best-fit values in this work. The top axis for the inset panels is the corresponding stellar mass at different v Mpeak. Top left: the best-fit r  c  in this work (turquoise curve in Figure 7). r  c  → 1 at v Mpeak ≲ 100 km s−1, M ⋆ ≲ 109 M ⊙. Top right: using the r  c  values from UM DR1, which is the orange curve in Figure 7. With r  c  ∼ 0.5, the SAGA satellite f  Q  differs from the isolated field f  Q , but their differences are too small compared to observations. Bottom left: setting r  c  = 1, maximally allowed correlation between halo stripping and galaxy quenching, similar to UM-SAGA. Bottom right: setting r  c  = 0, no correlation between halo stripping and galaxy quenching; SFR only depends on halo mass. The model f  Q  for isolated field galaxies is identical to SAGA-like satellites in this case, both being the same as f  Q,all. Comparing the two left panels, UM-SAGA almost requires the maximally allowed correlation strength between SFR and ﹩{\rm{\Delta }}{v}_{\max }﹩ to capture the large difference in f  Q  for SAGA satellites and isolated field galaxies.
Paper: The SAGA Survey. V. Modeling Satellite Systems around Milky Way–Mass Galaxies with Updated UNIVERSEMACHINE 
Authors: Yunchong Wang, Ethan O. Nadler, Yao-Yuan Mao, Risa H. Wechsler, Tom Abel, Peter Behroozi, Marla Geha, Yasmeen Asali, Mithi A. C. de los Reyes, Erin Kado-Fong, Nitya Kallivayalil, Erik J. Tollerud, Benjamin Weiner, John F. Wu
Figure caption: The rank correlation coefficient r  c (v Mpeak) between halo accretion (﹩{\rm{\Delta }}{v}_{\max }﹩) and SFR. Turquoise is the UM-SAGA model, while orange is the UM DR1 (shaded error bars denote darker 68% and lighter 95% distributions of the parameter posteriors). The UM-SAGA r  c  approaches the UM DR1 best-fit value in the halo mass range where galaxy clustering constraints were available at M ⋆ ≥ 1010.3 M ⊙. At the low-mass end, the new model requires r  c  → 1 to simultaneously match the high quenched fractions of SAGA satellites and f  Q  ∼ 0 for SDSS isolated field galaxies. The model is thus approaching the strongest possible correlation between halo assembly and SFR toward low masses.
Paper: The SAGA Survey. V. Modeling Satellite Systems around Milky Way–Mass Galaxies with Updated UNIVERSEMACHINE 
Authors: Yunchong Wang, Ethan O. Nadler, Yao-Yuan Mao, Risa H. Wechsler, Tom Abel, Peter Behroozi, Marla Geha, Yasmeen Asali, Mithi A. C. de los Reyes, Erin Kado-Fong, Nitya Kallivayalil, Erik J. Tollerud, Benjamin Weiner, John F. Wu
Figure caption: The best-fit UM-SAGA SMHM relation (turquoise) compared to that predicted by UM DR1 (orange) for objects in the c125-2048 cosmological simulation. The solid curves denote all galaxies, the dashed curves denote SAGA-like satellites, and the dotted curves denote SDSS-like isolated field galaxies; shaded bands show 68% scatter in each M peak bin. The UM-SAGA model predicts an almost identical SMHM relation compared to UM DR1 down to M ⋆ ∼ 106 M ⊙, still consistent with abundance matching constraints inferred from the MW satellite population (gray bands are 68% and 95% marginalized SMHM uncertainties from E. O. Nadler et al. 2020). The horizontal dotted line indicates the stellar mass below which the UM-SAGA SMHM relation is an unconstrained extrapolation. The vertical dashed line is the resolution limit of 300× particle mass in our cosmological simulation.
Paper: The SAGA Survey. V. Modeling Satellite Systems around Milky Way–Mass Galaxies with Updated UNIVERSEMACHINE 
Authors: Yunchong Wang, Ethan O. Nadler, Yao-Yuan Mao, Risa H. Wechsler, Tom Abel, Peter Behroozi, Marla Geha, Yasmeen Asali, Mithi A. C. de los Reyes, Erin Kado-Fong, Nitya Kallivayalil, Erik J. Tollerud, Benjamin Weiner, John F. Wu
Figure caption: Model predictions of satellite quenching radial trends. The SAGA data in this panel were not directly used to constrain the model. Top panel: specific SFR vs. 2D projected distance to the host. Green diamonds with error bars are the average and intrinsic scatter of NUV sSFRs for SAGA star-forming satellites. The scatter points are individual SAGA satellites, with NUV sSFR measurements shown as circles and NUV upper limits shown as triangles. The gray contours show the 10th percentiles of the UM-SAGA prediction. Both the model and the data have rather flat radial sSFR trends. Bottom panel: projected 2D cumulative radial number distribution of SAGA quenched (red) and star-forming (blue) satellites. Solid curves are for observations (completeness corrected), and dashed curves are for the model (2D). The dotted–dashed curves show the 3D profiles of true bounded satellites in UM-SAGA. The shaded region around each curve indicates jackknife errors.