adsabs/AstroCLIMB huggingface dataset:
Paper: Interior Convection Regime, Host Star Luminosity, and Predicted Atmospheric CO2 Abundance in Terrestrial Exoplanets
Authors: Antonin Affholder, Stéphane Mazevet, Boris Sauterey, Dániel Apai, Régis Ferrière
Figure caption: (A) Photochemical rates of production (solid lines) and alteration (dashed lines) of molecules included in our coupled model calculated from our interpolation of Atmos outputs or calculated from the parameterization in (B) (Sauterey et al. 2020; semitransparent lines). These calculations assume S/S⊕ = 0.8, the value of the luminosity assumed for the Archean Earth in the parameterization of (B) (Sauterey et al. 2020). Other gases are fixed at pCH4 = 0.1 ppm and pH2 = 100 ppm, average values in our simulations. (B) Sign of the photochemical flux of CO2 as a function of atmospheric concentration in CO2 and relative luminosity. Dark areas represent pCO2 luminosity conditions in which the photochemistry results in net removal of CO2, and the white region of the plot represent conditions in which photochemistry results in net production of CO2. These calculations use fixed pCH4 = 0.01 ppm and pH2 = 35 ppm, representative of SL simulations with age >2 Gyr that have a high final CO2 (​​​​​​>0.1 bar).
Paper: Interior Convection Regime, Host Star Luminosity, and Predicted Atmospheric CO2 Abundance in Terrestrial Exoplanets
Authors: Antonin Affholder, Stéphane Mazevet, Boris Sauterey, Dániel Apai, Régis Ferrière
Figure caption: (A) Comparison of HZ results. The simulations are represented as crosses when they have converged and circles when they have not. In the cases that did not converge, the temperature retained and plotted is representative (the last recorded iteration of the climate simulation) but does not correspond to an atmosphere in radiative equilibrium. The color code of the simulations is shown in the legend and corresponds to the partial pressure of CO2 forced constant at the bottom of the atmosphere. The blue dashed lines correspond to the outer edge of the HZ. The horizontal one is set at T = 0°C, and the vertical one is set at the outer HZ limit (OHZ) S/S⊕ = 0.46 value calculated in R. K. Kopparapu et al. (2013) for pCO2 = 1 bar. The orange dashed lines represent the inner limit of the HZ (IHZ): horizontally at the surface temperature TS = 340 K at which the moist greenhouse or water loss limit is reached (Section 2.1) and vertically at the consensus luminosity at which this limit is reached, S/S⊕ = 1.015 (R. K. Kopparapu et al. 2013). The solid red vertical line corresponds to the greenhouse runaway limit S/S⊕ = 1.06 (R. K. Kopparapu et al. 2013). (B) Comparison of climate simulations from the Atmos model used here (black circles) and previously published simulations for an early Earth at S/S⊕ = 0.8 (approximately 2.8 billion yr ago; J. D. Haqq-Misra et al. 2008; P. von Paris et al. 2008; B. Charnay et al. 2013; E. Wolf & O. Toon 2013). These calculations assume fixed abundances pH2 = 100 ppm and pCH4 = 0.1 ppm, which are the average values in our simulations with age τ ∈ [2, 3] Gyr and S/S⊕ ∈ [0.75, 0.85].
Paper: Interior Convection Regime, Host Star Luminosity, and Predicted Atmospheric CO2 Abundance in Terrestrial Exoplanets
Authors: Antonin Affholder, Stéphane Mazevet, Boris Sauterey, Dániel Apai, Régis Ferrière
Figure caption: Decomposition of linear correlation coefficients and inferred slope of different prior variables. (A)–(C) Correlation coefficients for ML simulations (A), SL simulations (B), and “young” (<2 Gyr) SL simulations (C). Stars indicate variables for which a linear correlation is found at a significance threshold p < 0.05 (two-sided Wald test). (D)–(F) Inferred slopes of the different predictor variables for ML simulations (D), SL simulations (E), and “young” (<2 Gyr) SL planets. Variables not pictured were found to have p-values exceeding the 0.05 significance threshold in all three cases or to correlate at the 0.05 significance level but with R2 < 0.01. Note that correlations with p﹩{{\rm{CO}}}_{2}^{0}﹩, Fout, ﹩{F}_{{\rm{o}}{\rm{u}}{\rm{t}}}^{{\rm{M}}{\rm{E}}}﹩, and Volc are performed on ﹩{\mathrm{log}}_{10}﹩-transformed values of those variables.
Paper: Interior Convection Regime, Host Star Luminosity, and Predicted Atmospheric CO2 Abundance in Terrestrial Exoplanets
Authors: Antonin Affholder, Stéphane Mazevet, Boris Sauterey, Dániel Apai, Régis Ferrière
Figure caption: Climate calculations in the pCO2 vs. pCH4 space for S/S⊕ = 0.8 and pH2 = 10−5 bars. The dots represent simulations made with the photochemistry/climate-coupled model, and their color reflects the calculated surface temperature at radiative equilibrium.
Paper: Orbits of Six Triple Systems
Authors: Andrei Tokovinin
Figure caption: Fragment of the FEROS spectrum of BS Ind around the sodium D lines taken on 2017 August 3. Note the narrow lines of A and the wide and blended lines of Ba and Bb. The spectrum was downloaded from the ESO archive. The flux units are arbitrary.
Paper: Orbits of Six Triple Systems
Authors: Andrei Tokovinin
Figure caption: The outer orbit (top) and the TESS light curves (LCs; bottom) of BS Ind.
Paper: Interior Convection Regime, Host Star Luminosity, and Predicted Atmospheric CO2 Abundance in Terrestrial Exoplanets
Authors: Antonin Affholder, Stéphane Mazevet, Boris Sauterey, Dániel Apai, Régis Ferrière
Figure caption: Probabilistic calculations for the inference of the dominant interior convection regime. (A) Estimate of the conditional probability P(Γ∣S/S⊕) for each interior convection scenario used in Equation (38). (B) Inference risk (Equation (35)) over strategies of CO2 detection threshold (ζ) and sample size (n). Orange (blue) lines indicate 1% (5%) risk contour levels. Solid lines map the risk that evidence in favor of the correct hypothesis is less than “extreme” (BF < 100). Dashed lines are the risk that evidence in favor of the correct hypothesis is less than “strong” (BF < 10). Semitransparent dashed and dotted lines are the risk that a sample yields stronger evidence in favor of the incorrect hypothesis than in favor of the correct one. The inset at the bottom shows the expected yield of different concepts of space observatories: LUVOIR (The LUVOIR Team 2019); HE, or HabEx (B. S. Gaudi et al. 2020); and HWO (E. Mamajek & K. Stapelfeldt 2024). The gray lines and circles indicate the expected number of EECs detected by each mission. Orange dots indicate the expected number of EEC spectra from each mission. The red dot indicates the target EEC spectra yield from the most recently proposed mission concept, HWO.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The median model fit resulting from testing nuts-for-ysos on HST STIS G430L and G750L data of Sz97, considering 14 different continuum features between ∼3300 Å and ∼6000 Å.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The median values of each photospheric template flux, after being multiplied by its squared distance. A fourth-degree polynomial is fit to the medians, and the template fluxes and respective luminosities are then rescaled to match this polynomial.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: Comparison of Kslab posteriors (expressed as log(106*Kslab)) for Object 8 versus Object 10. In blue is the original Kslab posterior, using the uniform prior for Kslab that was introduced in Section 4.3.2 and Table 4. In orange is the posterior resulting from an altered Kslab prior that is uniform instead in logarithmic space. From this comparison, we see that Object 8 is possibly nonaccreting by virtue of having a slab component in the accretion model, which approaches zero.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: A diagram illustrating the components of the YSO model. Parameters that are fitted for are highlighted in yellow. The shape of each prior is shown using Kruschke-style plots. Each prior has a color-coded arrow indicating whether it belongs to the slab portion, the photospheric portion, or to the entire composite model (black, green, and blue, respectively).
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: Object 16 (ATO J052.3580+31.4444), the one YSO in our current sample for which we were unable to fit a model including accretion. Instead, we fit the spectrum (red) with only a reddened Class III template (green) having Teff ≈  5500K and AV ≈  3.2.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The YSO model fit to VIRUS spectra of class II and class III YSOs. On the top left of each plot is the object ID originating in Tables 1 and 10. The VIRUS spectrum (red) has a model fit to its continuum (blue), which is the sum of the hydrogen accretion slab (black) and the photospheric template (green).
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: Continuum-derived accretion luminosity Lacc of both the class II and class III sample plotted against line luminosity for a variety of emission lines studied in J. M. Alcalá et al. (2017). Each emission line is labeled on the top left of the plot. For the class II YSOs, detected lines are plotted in black, and lines with only upper limits are plotted in gray. For class III YSOs, all results are plotted in green with empty arrows. Measurements for objects 4 and 13, which have only an approximate Lacc, are represented with circles. The red dotted line represents the relation from J. M. Alcalá et al. (2017), and is not a line fitted to our data.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: Plot of log(Lacc) vs. log(L*) for the class II and class III YSO targets. Class II YSOs are plotted in black, and the class III objects are plotted in green. Upper limits on logLacc are denoted with downward arrows instead of error bars. Object 12 has an upper limit for log(Lacc), but it is so low ( ≈ −6.3) that it falls below the plot limits. Object 4 is shown as a black open circle, and Object 13 is shown as a green open circle. These two objects only have upper or lower limits on Teff, as explained in Section 5, so their L* and Lacc values are taken to be approximate.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: Plot of log(Macc) vs. log(M*) for our YSO sample, using masses derived from the I. Baraffe et al. (2015) evolutionary model. Class II YSOs are shown in black, and class III YSOs are shown in green. Objects are annotated with their indices according to Table 10. Upper limits on log(Macc) are denoted with downward arrows. Object 4 does not fall within the mass range of I. Baraffe et al. (2015) and is therefore not included in this plot. Object 13 is represented by a green open circle because its log(M*) and log(Macc) are approximate.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: The average Lacc,line plotted against the continuum-derived Lacc for the class II stars (black) as well as Object 13, a class III star (green). Lacc,line is derived using emission-line luminosities and the relations from J. M. Alcalá et al. (2017). Objects 4 and 13, having only an approximate Lacc and Lacc,line, are represented with open circles. The red dotted line illustrates the approximate 1:1 correlation.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: Plot of log(Macc) vs. log(M*) for our YSO sample, using masses derived from the L. Siess et al. (2000) evolutionary model. Plotting conventions are the same as in Figure 11. Object 13 does not fall within the mass range of L. Siess et al. (2000) and is not included in this plot. Object 4 is represented by a black open circle because its log(M*) and log(Macc) are approximate.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: Continuum-derived accretion luminosity Lacc of both the class II and class III sample plotted against line luminosity for a variety of emission lines studied in J. M. Alcalá et al. (2017). The plot conventions are the same as Figure 13.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: Hertzsprung–Russell diagram for the total sample, plotted over evolutionary tracks (blue lines) and isochrones (dashed red lines) from I. Baraffe et al. (2015). Class II YSOs are plotted in black, and class III YSOs are plotted in green. Objects 4 and 13 have only approximate L* and are therefore shown as open circles, with arrows to signify their respective lower and upper limits on Teff.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: Hertzsprung–Russell diagram for the total sample, plotted over evolutionary tracks (blue lines) and isochrones (dashed red lines) from L. Siess et al. (2000). Plotting conventions are the same as in Figure 9.
Paper: A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS 
Authors: Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
Figure caption: An example corner plot for the fit of an accreting YSO model to the spectrum of Object 11 (2MASS J03285101+3118184). Included are the model parameters as well as the log(L*) and log(Lacc) posteriors. The corner plots for all fits are given in Appendix B.
Paper: Improving Radial Velocities by Marginalizing over Stars and Sky: Achieving 30 m s−1 RV Precision for APOGEE in the Plate Era
Authors: Andrew K. Saydjari, Douglas P. Finkbeiner, Adam J. Wheeler, Jon A. Holtzman, John C. Wilson, Andrew R. Casey, Sophia Sánchez-Maes, Joel R. Brownstein, David W. Hogg, Michael R. Blanton
Figure caption: Log-density histograms of the scatter in Barycentric velocity for repeat visits of the same star obtained by apMADGICS vs. the median signal-to-noise ratio (S/N) of the visits. Repeats are subdivided by telescope, APO (left) and LCO (right), and by repeat observations using any fiber (top) or restricted to observations using the same fiber (bottom). The average velocity scatter vs. S/N is shown for the DRP (black solid line) and apMADGICS (green solid line). The median velocity scatter at high S/N (120–450) is indicated by a dashed line matched in color and labeled rounded to the nearest m s−1. Cuts to targets based on ASPCAP quality flags, stellar type, and σ(vbary) < 1 km s−1 to reject outliers were applied (see the text for more).
Paper: Improving Radial Velocities by Marginalizing over Stars and Sky: Achieving 30 m s−1 RV Precision for APOGEE in the Plate Era
Authors: Andrew K. Saydjari, Douglas P. Finkbeiner, Adam J. Wheeler, Jon A. Holtzman, John C. Wilson, Andrew R. Casey, Sophia Sánchez-Maes, Joel R. Brownstein, David W. Hogg, Michael R. Blanton
Figure caption: Top-left panel: logarithmic histogram of visits in APOGEE DR17 passing quality cuts vs. estimates of Teff and ﹩\mathrm{log}\,g﹩ from ASPCAP. Bottom-left panel: median S/N per visit. Middle-top panel: theoretical RV lower bound if all visits were S/N 100. Middle-bottom panel: theoretical RV lower bound given observed per-pixel uncertainties. Top-right panel: scatter in barycentric velocity for repeat visits, irrespective of the fiber on which a star was measured. Bottom-right panel: scatter in barycentric velocity for repeat visits restricted to using the same fiber. The conventionally quoted RV precision for APOGEE of 30 m s−1 is indicated for reference by a vertical line on the color bar.
Paper: Improving Radial Velocities by Marginalizing over Stars and Sky: Achieving 30 m s−1 RV Precision for APOGEE in the Plate Era
Authors: Andrew K. Saydjari, Douglas P. Finkbeiner, Adam J. Wheeler, Jon A. Holtzman, John C. Wilson, Andrew R. Casey, Sophia Sánchez-Maes, Joel R. Brownstein, David W. Hogg, Michael R. Blanton
Figure caption: Same as Figure 2, but with the scatter measured relative to the maximum likelihood estimate for the “true” RV for each star, ﹩{\hat{v}}_{{\rm{ref}}}﹩. The average velocity scatter vs. S/N is shown for the DRP (black solid line), apMADGICS (green solid line), and apMADGICS after correcting for constant RV fiber–fiber offsets (blue solid line).
Paper: Improving Radial Velocities by Marginalizing over Stars and Sky: Achieving 30 m s−1 RV Precision for APOGEE in the Plate Era
Authors: Andrew K. Saydjari, Douglas P. Finkbeiner, Adam J. Wheeler, Jon A. Holtzman, John C. Wilson, Andrew R. Casey, Sophia Sánchez-Maes, Joel R. Brownstein, David W. Hogg, Michael R. Blanton
Figure caption: Top panel: constant RV offset as a function of fiber number. The dashed line divides the fiber axis between the two telescopes. The light-gray lines show the boundaries between physical v-groove blocks of 30 fibers. Bottom-left panel: adjacency matrix showing number of visits to stars in common between two different fibers. The patchwork pattern is partially due to fiber blocks, which are discussed further in Section 5.1. Bottom-right panel: inverse curvature matrix representing uncertainty in the estimation of the per fiber RV offsets.
Paper: Improving Radial Velocities by Marginalizing over Stars and Sky: Achieving 30 m s−1 RV Precision for APOGEE in the Plate Era
Authors: Andrew K. Saydjari, Douglas P. Finkbeiner, Adam J. Wheeler, Jon A. Holtzman, John C. Wilson, Andrew R. Casey, Sophia Sánchez-Maes, Joel R. Brownstein, David W. Hogg, Michael R. Blanton
Figure caption: Logarithmic 2D histograms of the deviation of the observed barycentric velocity (vbary) from the average for a given star shown as a function of the sky (observatory) frame velocity (vrel) for the DRP (left) and apMADGICS (middle). These plots are restricted to stars with more than five visits with good RV measurements. The last panel shows the difference between the barycentric velocity from the DRP and apMADGICS for the same visit as a function of the observed (sky frame) velocity. The top and bottom rows show different velocity scales on both axes.
Paper: Improving Radial Velocities by Marginalizing over Stars and Sky: Achieving 30 m s−1 RV Precision for APOGEE in the Plate Era
Authors: Andrew K. Saydjari, Douglas P. Finkbeiner, Adam J. Wheeler, Jon A. Holtzman, John C. Wilson, Andrew R. Casey, Sophia Sánchez-Maes, Joel R. Brownstein, David W. Hogg, Michael R. Blanton
Figure caption: Log-density histograms of formal radial velocity errors from apMADGICS vs. the median S/N of the visits of a given star for observations at APO (left) and LCO (right). The average formal error is shown as a green dotted line, and the average observed scatter for repeat measurements is shown as a green solid line. The average “calibrated” RV errors for apMADGICS are shown as a dashed green line, and agree well with the observed scatter from repeats. The average reported error from the DRP is shown as a dashed black line, and the average observed scatter for repeat measurements from the DRP is shown as a solid black line.
Paper: Improving Radial Velocities by Marginalizing over Stars and Sky: Achieving 30 m s−1 RV Precision for APOGEE in the Plate Era
Authors: Andrew K. Saydjari, Douglas P. Finkbeiner, Adam J. Wheeler, Jon A. Holtzman, John C. Wilson, Andrew R. Casey, Sophia Sánchez-Maes, Joel R. Brownstein, David W. Hogg, Michael R. Blanton
Figure caption: For stars with at least five good RV visits, the deviation of the barycentric velocity obtained for a single visit from the average barycentric velocity for the star is shown as a 2D histogram vs. the fiber used for that visit (top panel are APO fibers, bottom panel are LCO fibers). The 2D histogram is column normalized, and the color is logarithmic. Guidelines (gray) indicate the edges of the physical fiber blocks.
Paper: Improving Radial Velocities by Marginalizing over Stars and Sky: Achieving 30 m s−1 RV Precision for APOGEE in the Plate Era
Authors: Andrew K. Saydjari, Douglas P. Finkbeiner, Adam J. Wheeler, Jon A. Holtzman, John C. Wilson, Andrew R. Casey, Sophia Sánchez-Maes, Joel R. Brownstein, David W. Hogg, Michael R. Blanton
Figure caption: Average RV precision per observing run.
Paper: Variations of Absolute Source Positions Determined from Quad-band VLBI Observations
Authors: Ming Hui Xu, Patrick Charlot
Figure caption: Stability of the quad-band source positions as a function of decl. for R.A.∗ (upper panel) and for decl. (lower panel). The standard deviations of the coordinate time series with respect to the weighted means are shown as black dots, whereas the wrms values of the coordinate residuals with respect to the smoothing models are shown as red dots. The lower edge of the distribution of the red dots characterizes the precision that one can reach at most in the determination of source positions and is shown as a gray curve in each plot. The instability above the gray curve is mainly caused by source structure, at least for the sources with decl. above 20°, whereas the gray curve itself indicates the level of the error contributions due to other effects, such as tropospheric path delays and limitation on the sensitivity of the VGOS network to position coordinates.
Paper: Variations of Absolute Source Positions Determined from Quad-band VLBI Observations
Authors: Ming Hui Xu, Patrick Charlot
Figure caption: Distribution of positions of source 1803+784 at 118 epochs determined from a global solution of quad-band VLBI observations. The positions are relative to the S/X position of 1803+784 in the ICRF3. Color coding depicts the epoch of each of these 118 VLBI sessions. The standard deviation of the position estimates with respect to the weighted mean is 0.04 mas for both coordinates, as written in the upper right-hand corner. Position estimates with a strong correlation (>0.7) between R.A. and decl. are not shown because they rely on only a few observations and have very large uncertainties.