adsabs/AstroCLIMB huggingface dataset:
Paper:  ethraid: A Simple Method for Characterizing Long-period Companions Using Doppler, Astrometric, and Imaging Constraints
Authors: Judah Van Zandt, Erik A Petigura
Figure caption: Same as Figure 4 for TOI-1694's two-year baseline. The gray lines in panels (d)–(f) show posteriors derived from direct imaging. The gold stars show TOI-1694 b’s median semimajor axis and adjusted ﹩{m}_{c}\sin i﹩ values measured by Van Zandt et al. (2023): a = 0.98 au, ﹩{m}_{c}\sin i=1.05﹩                      M J. Without astrometry, the trend and imaging data provide too little information to constrain the planet’s mass and separation. However, they are able to rule out massive stars as well as the (a, m  c ) pairs in the white regions. Note also that the posteriors in panels (e) and (f) favor shorter-period models, due to the higher RV curvature associated with their trends.
Paper:  ethraid: A Simple Method for Characterizing Long-period Companions Using Doppler, Astrometric, and Imaging Constraints
Authors: Judah Van Zandt, Erik A Petigura
Figure caption: Same as Figure 4 for HD 114729. The gold star at the lower left of each panel shows the semimajor axis and adjusted ﹩{m}_{c}\sin i﹩ of HD 114729 Ab measured by long-baseline RV surveys: 2.094 au, ﹩{m}_{c}\sin i=0.892﹩                      M J. The upper right star shows the same for HD 114729 B, with a projected separation and mass of 282 au and M = 0.253 M ⊙. We estimated HD 114729 B’s true separation by dividing the projected separation of 282 au by 0.79, as described in Section 3.3. Agreement between the planet parameters and the RV posteriors indicates that ethraid is correctly recovering HD 114729 Ab’s RV signature. Meanwhile, the consistency between HD 114729 B’s parameters and the astrometric posterior suggests that HD 114729 B produced the astrometric trend. Note that, in panels (a) and (d), the fitted trend/curve values had precisions <2σ, leading to broad RV posteriors in panels (e) and (h).
Paper:  ethraid: A Simple Method for Characterizing Long-period Companions Using Doppler, Astrometric, and Imaging Constraints
Authors: Judah Van Zandt, Erik A Petigura
Figure caption: Left: Panel (d) from Figure 5, showing the companion models consistent with a subset of the RVs of TOI-1694, as well as the imaging data. Note that, despite falling above the gray 95% contour, some high-mass models are consistent with the imaging data because their orbital geometries result in small projected angular separations from the host star. Right: Parameter constraints for the same RV and imaging data, but using the approximate imaging calculation. In the approximate case, the imaging contour represents a hard bound, above which companions are ruled out with certainty. This causes the 1σ upper bound on mass (dark red) to be underestimated by ∼30% at 16 au, while being roughly unaffected at 8 au.
Paper:  ethraid: A Simple Method for Characterizing Long-period Companions Using Doppler, Astrometric, and Imaging Constraints
Authors: Judah Van Zandt, Erik A Petigura
Figure caption: Same as Figure 4 for HD 12661. For this system, we include RVs from both the Keck/HIRES and APF/Levy instruments. The gold stars show the a and adjusted ﹩{m}_{c}\sin i﹩ values of HD 12661 b (left) and c (right). Planet b dominates the RV signature, due to its short period, as shown by its consistency with the green RV posteriors in each slice. Meanwhile, planet c is responsible for the majority of the astrometric signal. The inconsistency between these planets’ measured parameters and the combined posterior (red) shows that ethraid misinterpreted these separate contributions as originating from a single object of higher mass.
Paper:  ethraid: A Simple Method for Characterizing Long-period Companions Using Doppler, Astrometric, and Imaging Constraints
Authors: Judah Van Zandt, Erik A Petigura
Figure caption: Our model of astrometric proper motion anomaly. Red ellipses show the stellar orbit associated with a nonluminous bound companion (not shown). Green arrows show the average proper motion vectors fitted to the data from Hipparcos (left) and Gaia (right). Gold stars show the average positions during Gaia and Hipparcos, and the blue arrow shows  μ  HG, the average proper motion between them, calculated as the difference in average positions divided by the difference between the mission midpoints. The proper motion anomaly Δμ, shown in red, quantifies the difference between the average Gaia proper motion and the position-derived proper motion. Figure adapted from Kervella et al. (2019).
Paper:  ethraid: A Simple Method for Characterizing Long-period Companions Using Doppler, Astrometric, and Imaging Constraints
Authors: Judah Van Zandt, Erik A Petigura
Figure caption:  ethraid's likelihood calculation for direct imaging. Beginning with a set of sampled parameters, ethraid models angular separation (either exactly or approximately) using the orbital elements, and contrast using the companion mass, together with a linear interpolation function derived from Table 5 of Pecaut & Mamajek (2013) and Table 4 of Baraffe et al. (2003), denoted by “PM13” and “B03,” respectively. ethraid then determines whether the model companion’s brightness would have exceeded the minimum detectability threshold at the model separation in the real imaging data (gold line) and assigns a likelihood of 0 for detectable companions and 1 for nondetectable companions. The example model in the diagram falls above the threshold, so we assign it a likelihood of 0.
Paper: 89 New Ultracool Dwarf Comoving Companions Identified with the Backyard Worlds: Planet 9 Citizen Science Project
Authors: Austin Rothermich, Jacqueline K. Faherty, Daniella Bardalez-Gagliuffi, Adam C. Schneider, J. Davy Kirkpatrick, Aaron M. Meisner, Adam J. Burgasser, Marc Kuchner, Katelyn Allers, Jonathan Gagné, Dan Caselden, Emily Calamari, Mark Popinchalk, Genaro Suárez, Roman Gerasimov, Christian Aganze, Emma Softich, Chin-Chun Hsu, Preethi Karpoor, Christopher A. Theissen, Jon Rees, Rosario Cecilio-Flores-Elie, Michael C. Cushing, Federico Marocco, Sarah Casewell, Thomas P. Bickle, Les Hamlet, Michaela B. Allen, Paul Beaulieu, Guillaume Colin, Jean Marc Gantier, Leopold Gramaize, Peter Jalowiczor, Martin Kabatnik, Frank Kiwy, David W. Martin, Billy Pendrill, Ben Pumphrey, Arttu Sainio, Jorg Schumann, Nikolaj Stevnbak, Guoyou Sun, Christopher Tanner, Vinod Thakur, Melina Thevenot, Zbigniew Wedracki
Figure caption: Left: CMD of potential companion objects using WISE photometry, adopting their host’s parallax if necessary. Plotted with the sample are known ultracool dwarfs with parallaxes taken from The UltracoolSheet (Best et al. 2020), color coded by spectral type. Right: CMD of potential host objects (as blue stars) using Gaia DR3 photometry. Any suspected companions with Gaia DR3 parallaxes and astrometry are plotted as pink stars. Plotted for reference is the Gaia DR3 30 pc sample.
Paper: 89 New Ultracool Dwarf Comoving Companions Identified with the Backyard Worlds: Planet 9 Citizen Science Project
Authors: Austin Rothermich, Jacqueline K. Faherty, Daniella Bardalez-Gagliuffi, Adam C. Schneider, J. Davy Kirkpatrick, Aaron M. Meisner, Adam J. Burgasser, Marc Kuchner, Katelyn Allers, Jonathan Gagné, Dan Caselden, Emily Calamari, Mark Popinchalk, Genaro Suárez, Roman Gerasimov, Christian Aganze, Emma Softich, Chin-Chun Hsu, Preethi Karpoor, Christopher A. Theissen, Jon Rees, Rosario Cecilio-Flores-Elie, Michael C. Cushing, Federico Marocco, Sarah Casewell, Thomas P. Bickle, Les Hamlet, Michaela B. Allen, Paul Beaulieu, Guillaume Colin, Jean Marc Gantier, Leopold Gramaize, Peter Jalowiczor, Martin Kabatnik, Frank Kiwy, David W. Martin, Billy Pendrill, Ben Pumphrey, Arttu Sainio, Jorg Schumann, Nikolaj Stevnbak, Guoyou Sun, Christopher Tanner, Vinod Thakur, Melina Thevenot, Zbigniew Wedracki
Figure caption: Spectra obtained with SpeX for low-probability companions plotted in black. Overplotted in color (with redder colors indicating cooler spectral temperatures) are the spectral standards as described in Section 5. All spectra have been normalized between 1.27 and 1.29 μm and separated by a constant.
Paper: Estimating Galaxy Parameters with Self-organizing Maps and the Effect of Missing Data
Authors: Valentina La Torre, Anna Sajina, Andy D. Goulding, Danilo Marchesini, Rachel Bezanson, Alan N. Pearl, Laerte Sodré
Figure caption: Top: The density of galaxies in the stellar mass vs. redshift plane from the mass-limited 1 deg2 simulated Horizon-AGN lightcone, after applying a u-magnitude cut of m  u  < 28 (Section 2.1). Bottom: The same simulated data set after color calibration and applying a K  s -magnitude cut of K  s  < 23.5 (Section 2.2).
Paper: Estimating Galaxy Parameters with Self-organizing Maps and the Effect of Missing Data
Authors: Valentina La Torre, Anna Sajina, Andy D. Goulding, Danilo Marchesini, Rachel Bezanson, Alan N. Pearl, Laerte Sodré
Figure caption:  M/L  ν,u  ratio for the observed u band vs. the sSFRUV in the redshift bin 0.8 < z < 0.9, before and after color calibration in the upper and lower panels, respectively. The black data points and their relative best fits (black solid lines) represent the simulations. The red dashed line represents the best fit to the observed UVISTA catalog (Muzzin et al. 2013). These best fits are used to adjust the simulated photometry to match the observed relation. We perform such color calibration for all bands in all redshift bins.
Paper: NGC 1856: Using Machine Learning Techniques to Uncover Detailed Stellar Abundances from MUSE Data
Authors: Randa Asa'd, S. Hernandez, J. M John, M. Alfaro-Cuello, Z. Wang, A. As'ad, A. Vasini, F. Matteucci
Figure caption: Abundance ratios as a function of [Fe/H]. Solid green circles show the abundances inferred in this work. Solid orange and light-blue circles are the literature data from field stars in the LMC bar and inner-disk abundances, respectively, from Van der Swaelmen et al. (2013). The solid black line shows the chemical evolution model (Model 3 by Vasini et al. 2023 (see their Table 1). In the bottom left panel, the solid red line illustrates the carbon yields from massive stars, which have been adjusted by a factor of 2 as part of an “ad hoc” correction strategy to enhance alignment with observed data.
Paper: NGC 1856: Using Machine Learning Techniques to Uncover Detailed Stellar Abundances from MUSE Data
Authors: Randa Asa'd, S. Hernandez, J. M John, M. Alfaro-Cuello, Z. Wang, A. As'ad, A. Vasini, F. Matteucci
Figure caption: Left panel: color–magnitude diagram. The red points show the stars in the photometric catalog by Correnti et al. (2015), overplotted are the stars in our final sample color coded by metallicity. Magnitudes F438W and F418W are sourced from the photometric catalog by Correnti et al. (2015). Right panel: the effective temperature vs. log g of the stars in the final sample, color coded by metallicity.
Paper: NGC 1856: Using Machine Learning Techniques to Uncover Detailed Stellar Abundances from MUSE Data
Authors: Randa Asa'd, S. Hernandez, J. M John, M. Alfaro-Cuello, Z. Wang, A. As'ad, A. Vasini, F. Matteucci
Figure caption: Radial-velocity-distribution function for the candidate member stars of NGC 1856. The vertical dashed line indicates the mean value, and the blue shadowed region is the range of radial velocities adopted in this work.
Paper: NGC 1856: Using Machine Learning Techniques to Uncover Detailed Stellar Abundances from MUSE Data
Authors: Randa Asa'd, S. Hernandez, J. M John, M. Alfaro-Cuello, Z. Wang, A. As'ad, A. Vasini, F. Matteucci
Figure caption: Our inferred abundance ratios as a function of [Fe/H] excluding any results with the coefficient correlation lower than 0.5 as well as results obtained from extrapolation as explained above. The dotted line corresponds to 0.0 to guide the eye, and the dashed line represents the mean for each element.
Paper: NGC 1856: Using Machine Learning Techniques to Uncover Detailed Stellar Abundances from MUSE Data
Authors: Randa Asa'd, S. Hernandez, J. M John, M. Alfaro-Cuello, Z. Wang, A. As'ad, A. Vasini, F. Matteucci
Figure caption: Our inferred abundance ratios as a function of T eff excluding any results with the coefficient correlation lower than 0.5 as well as results obtained from extrapolation as explained above. The dotted line corresponds to 0.0 to guide the eye, and the dashed line represents the mean for each element.
Paper: NGC 1856: Using Machine Learning Techniques to Uncover Detailed Stellar Abundances from MUSE Data
Authors: Randa Asa'd, S. Hernandez, J. M John, M. Alfaro-Cuello, Z. Wang, A. As'ad, A. Vasini, F. Matteucci
Figure caption: Correlation between the mean of the abundances obtained in this work and by Asa’d et al. (2022) with their respective uncertainties. The red line represents the one to one relation.
Paper: NGC 1856: Using Machine Learning Techniques to Uncover Detailed Stellar Abundances from MUSE Data
Authors: Randa Asa'd, S. Hernandez, J. M John, M. Alfaro-Cuello, Z. Wang, A. As'ad, A. Vasini, F. Matteucci
Figure caption: The figure above displays the residuals of spectral fits for 40 of the final 327 stars, accompanied by the corresponding errors depicted in red. The x-axis represents wavelength in Armstrong, while the y-axis depicts flux.
Paper: NGC 1856: Using Machine Learning Techniques to Uncover Detailed Stellar Abundances from MUSE Data
Authors: Randa Asa'd, S. Hernandez, J. M John, M. Alfaro-Cuello, Z. Wang, A. As'ad, A. Vasini, F. Matteucci
Figure caption: Illustration of Cluster NGC1856: Stars in blue represent those observed by the Hubble Space Telescope (HST). The final sample (327 cluster members) highlighted in red.
Paper: NGC 1856: Using Machine Learning Techniques to Uncover Detailed Stellar Abundances from MUSE Data
Authors: Randa Asa'd, S. Hernandez, J. M John, M. Alfaro-Cuello, Z. Wang, A. As'ad, A. Vasini, F. Matteucci
Figure caption: The figure above displays the residuals of spectral fits for the last set of 47 stars from the final 327 stars, accompanied by the corresponding errors depicted in red. The x-axis represents wavelength in Armstrong, while the y-axis depicts flux.
Paper: NGC 1856: Using Machine Learning Techniques to Uncover Detailed Stellar Abundances from MUSE Data
Authors: Randa Asa'd, S. Hernandez, J. M John, M. Alfaro-Cuello, Z. Wang, A. As'ad, A. Vasini, F. Matteucci
Figure caption: The figure above displays the residuals of spectral fits for the fifth set of 40 stars from the final 327 stars, accompanied by the corresponding errors depicted in red. The x-axis represents wavelength in Armstrong, while the y-axis depicts flux.
Paper: NGC 1856: Using Machine Learning Techniques to Uncover Detailed Stellar Abundances from MUSE Data
Authors: Randa Asa'd, S. Hernandez, J. M John, M. Alfaro-Cuello, Z. Wang, A. As'ad, A. Vasini, F. Matteucci
Figure caption: The figure above displays the residuals of spectral fits for the fourth set of 40 stars from the final 327 stars, accompanied by the corresponding errors depicted in red. The x-axis represents wavelength in Armstrong, while the y-axis depicts flux.
Paper: NGC 1856: Using Machine Learning Techniques to Uncover Detailed Stellar Abundances from MUSE Data
Authors: Randa Asa'd, S. Hernandez, J. M John, M. Alfaro-Cuello, Z. Wang, A. As'ad, A. Vasini, F. Matteucci
Figure caption: The figure above displays the residuals of spectral fits for the sixth set of 40 stars from the final 327 stars, accompanied by the corresponding errors depicted in red. The x-axis represents wavelength in Armstrong, while the y-axis depicts flux.
Paper: NGC 1856: Using Machine Learning Techniques to Uncover Detailed Stellar Abundances from MUSE Data
Authors: Randa Asa'd, S. Hernandez, J. M John, M. Alfaro-Cuello, Z. Wang, A. As'ad, A. Vasini, F. Matteucci
Figure caption: The figure above displays the residuals of spectral fits for the seventh set of 40 stars from the final 327 stars, accompanied by the corresponding errors depicted in red. The x-axis represents wavelength in Armstrong, while the y-axis depicts flux.
Paper: NGC 1856: Using Machine Learning Techniques to Uncover Detailed Stellar Abundances from MUSE Data
Authors: Randa Asa'd, S. Hernandez, J. M John, M. Alfaro-Cuello, Z. Wang, A. As'ad, A. Vasini, F. Matteucci
Figure caption: The figure above displays the residuals of spectral fits for the second set of 40 stars from the final 327 stars, accompanied by the corresponding errors depicted in red. The x-axis represents wavelength in Armstrong, while the y-axis depicts flux.
Paper: NGC 1856: Using Machine Learning Techniques to Uncover Detailed Stellar Abundances from MUSE Data
Authors: Randa Asa'd, S. Hernandez, J. M John, M. Alfaro-Cuello, Z. Wang, A. As'ad, A. Vasini, F. Matteucci
Figure caption: The figure above displays the residuals of spectral fits for the third set of 40 stars from the final 327 stars, accompanied by the corresponding errors depicted in red. The x-axis represents wavelength in Armstrong, while the y-axis depicts flux.
Paper: Age Analysis of Extrasolar Planets: Insight from Stellar Isochrone Models
Authors: C. Swastik, Ravinder K. Banyal, Mayank Narang, Athira Unni, T. Sivarani
Figure caption: Comparison of stellar ages computed from photometric T eff from Gaia DR3 and spectroscopic T eff from sweet-cat. The color coding represents the density of points.
Paper: Age Analysis of Extrasolar Planets: Insight from Stellar Isochrone Models
Authors: C. Swastik, Ravinder K. Banyal, Mayank Narang, Athira Unni, T. Sivarani
Figure caption: Stellar isochrone models generated using MIST. The red symbols represent planet-hosting stars. The isochrones are drawn for the solar-scaled abundances. Both X and Y scales are in logarithmic units.
Paper: Age Analysis of Extrasolar Planets: Insight from Stellar Isochrone Models
Authors: C. Swastik, Ravinder K. Banyal, Mayank Narang, Athira Unni, T. Sivarani
Figure caption: Distribution of giant planets as a function of stellar age. The color bars represent planet mass and labels in each plot window represent the isochrone models used. For all three cases, spectroscopic T eff and G magnitude were used as input parameters.
Paper: Age Analysis of Extrasolar Planets: Insight from Stellar Isochrone Models
Authors: C. Swastik, Ravinder K. Banyal, Mayank Narang, Athira Unni, T. Sivarani
Figure caption: Planet mass as a function of stellar age. The colorbar on the right represents the stellar metallicity values obtained from the literature. The dashed red line (M  P  = 0.3M  J ) separates the small and giant planet-hosting stars.
Paper: Age Analysis of Extrasolar Planets: Insight from Stellar Isochrone Models
Authors: C. Swastik, Ravinder K. Banyal, Mayank Narang, Athira Unni, T. Sivarani
Figure caption: Histogram for ages of the planets hosting stars for small and giant planets. The label is indicated in the following format: isochrone model-photometric-band temperature from spectroscopy or photometry. The dashed lines represent the median ages corresponding to their color labels in the histograms.
Paper: Age Analysis of Extrasolar Planets: Insight from Stellar Isochrone Models
Authors: C. Swastik, Ravinder K. Banyal, Mayank Narang, Athira Unni, T. Sivarani
Figure caption: Comparison of stellar ages from MIST model using V-band (Johnson) and G-band (Gaia) mag. The color coding represents the density of stars.
Paper: Age Analysis of Extrasolar Planets: Insight from Stellar Isochrone Models
Authors: C. Swastik, Ravinder K. Banyal, Mayank Narang, Athira Unni, T. Sivarani
Figure caption: Fraction of stars hosting giant planets to small planets as a function of stellar age using different models and input parameters. The data is binned at an interval of 1 Gyr.
Paper: Age Analysis of Extrasolar Planets: Insight from Stellar Isochrone Models
Authors: C. Swastik, Ravinder K. Banyal, Mayank Narang, Athira Unni, T. Sivarani
Figure caption: Top: comparison of ages obtained from stellar isochrones and from [α/Fe] abundances. Bottom: comparison of ages obtained from stellar isochrones and from Asteroseismology. The color represents the effective temperature of the star.
Paper: Age Analysis of Extrasolar Planets: Insight from Stellar Isochrone Models
Authors: C. Swastik, Ravinder K. Banyal, Mayank Narang, Athira Unni, T. Sivarani
Figure caption: Comparison of stellar ages from MIST, PARSEC, and YY isochrone fitting models. The color coding represents the density of stars.
Paper: Age Analysis of Extrasolar Planets: Insight from Stellar Isochrone Models
Authors: C. Swastik, Ravinder K. Banyal, Mayank Narang, Athira Unni, T. Sivarani
Figure caption: Cumulative distribution for the stellar ages of small (red) and giant (blue) planet-hosting stars obtained using different isochrone models and input parameters.
Paper: Differences between Stable and Unstable Architectures of Compact Planetary Systems
Authors: Kathryn Volk, Renu Malhotra
Figure caption: TTV/RV cases: distribution of period ratios between neighboring planets in 47 observed planetary system architectures with three or more planets and TTV or RV mass constraints on at least one planet (top: stacked histogram that sums to the observed number of planet pairs; bottom: normalized cumulative distributions). In gray are pairs of planets in system architectures where no collisions occurred within 5 × 109 orbits of the shortest-period planet across 100 instances of that architecture (initial conditions are described in Section 2.2). In pink are planet pairs in system architectures for which at least one of the 100 instances experienced a collision within 5 × 109 inner planet orbits.
Paper: Differences between Stable and Unstable Architectures of Compact Planetary Systems
Authors: Kathryn Volk, Renu Malhotra
Figure caption: MR-relationship cases: distribution of period ratios between neighboring planets in 162 observed planetary system architectures with three or more planets (top: stacked histogram that sums to the observed number of planet pairs; bottom: normalized cumulative distributions). In gray are pairs of planets in system architectures where no collisions occurred within 5 × 109 orbits of the shortest-period planet across 100 instances of that architecture (initial conditions are described in Section 2.1). In pink are period ratios in system architectures for which at least one of the 100 instances experienced a collision within 5 × 109 inner planet orbits.
Paper: Differences between Stable and Unstable Architectures of Compact Planetary Systems
Authors: Kathryn Volk, Renu Malhotra
Figure caption: TTV/RV cases: distribution of mutual hill radius separation (Δ) between neighboring planets in 47 observed planetary system architectures with three or more planets and TTV or RV mass constraints on at least one planet (top: stacked histogram weighted to the observed number of planet pairs; bottom: normalized cumulative distributions). In gray are pairs of planets in system architectures where no collisions occurred within 5 × 109 orbits of the shortest-period planet across 100 instances of that architecture (initial conditions are described in Section 2.2). In pink are planet pairs in system architectures for which at least one of the 100 instances experienced a collision within 5 × 109 inner planet orbits.
Paper: Differences between Stable and Unstable Architectures of Compact Planetary Systems
Authors: Kathryn Volk, Renu Malhotra
Figure caption: MR-relationship cases: distribution of mutual hill radius separation (Δ) between neighboring planets in 162 observed planetary system architectures with three or more planets (top: stacked histogram with simulations weighted to sum to the observed number of planet pairs; bottom: normalized cumulative distributions). In gray are pairs of planets in system architectures where no collisions occurred within 5 × 109 orbits of the shortest-period planet across 100 instances of that architecture (initial conditions are described in Section 2.1). In pink are planet pairs in system architectures for which at least one of the 100 instances experienced a collision within 5 × 109 inner planet orbits.