adsabs/AstroCLIMB huggingface dataset:
Paper: A Deep Search for Exomoons around WISE 0855 with JWST
Authors: Mikayla J. Wilson, Mary Anne Limbach, Andrew J. Skemer, Johanna M. Vos, Brittany E. Miles, Melanie J. Rowland, Andrew Vanderburg, Adam C. Schneider, Caroline Morley, Brooke Kotten, Andrew Householder, Roxana Lupu, James Mang, Richard Freedman
Figure caption: The light curves, normalized to 1, of regions 1 and 2 built from a weighted average calculated using empirical errors.
Paper: A Deep Search for Exomoons around WISE 0855 with JWST
Authors: Mikayla J. Wilson, Mary Anne Limbach, Andrew J. Skemer, Johanna M. Vos, Brittany E. Miles, Melanie J. Rowland, Andrew Vanderburg, Adam C. Schneider, Caroline Morley, Brooke Kotten, Andrew Householder, Roxana Lupu, James Mang, Richard Freedman
Figure caption: The original light curves of WISE 0855 built from the two different wavelength regions are shown in black. The solid pink and orange lines represent the fit of the GP + transit model. The dashed black line is the transit midtime recovered by the transit model. Panel (A) represents the original, unflipped version with no transit injections, and Panel (B) is the flipped version of the data with the corresponding GP + transit model fits. The blue line represents the best fit transit model as identified by the GP + transit model. In both the flipped and unflipped cases, we see the transit model is aligned with larger variability features that are expected to be intrinsic from the host.
Paper: A Deep Search for Exomoons around WISE 0855 with JWST
Authors: Mikayla J. Wilson, Mary Anne Limbach, Andrew J. Skemer, Johanna M. Vos, Brittany E. Miles, Melanie J. Rowland, Andrew Vanderburg, Adam C. Schneider, Caroline Morley, Brooke Kotten, Andrew Householder, Roxana Lupu, James Mang, Richard Freedman
Figure caption: Posterior parameter distributions of the GP + transit model results for the no-injection case on the unflipped light curves.
Paper: A Deep Search for Exomoons around WISE 0855 with JWST
Authors: Mikayla J. Wilson, Mary Anne Limbach, Andrew J. Skemer, Johanna M. Vos, Brittany E. Miles, Melanie J. Rowland, Andrew Vanderburg, Adam C. Schneider, Caroline Morley, Brooke Kotten, Andrew Householder, Roxana Lupu, James Mang, Richard Freedman
Figure caption: The original light curves of WISE 0855 built from the two different wavelength regions are shown in black. The solid lines represent the fit of the GP-only model. Panel (A) represents the original, unflipped version with no transit injections, and Panel (B) is the flipped version of the data with the corresponding GP-only model fits.
Paper: A Deep Search for Exomoons around WISE 0855 with JWST
Authors: Mikayla J. Wilson, Mary Anne Limbach, Andrew J. Skemer, Johanna M. Vos, Brittany E. Miles, Melanie J. Rowland, Andrew Vanderburg, Adam C. Schneider, Caroline Morley, Brooke Kotten, Andrew Householder, Roxana Lupu, James Mang, Richard Freedman
Figure caption: Posterior parameter distributions of the GP-only models for the example case described in Section 4.2. Both regions are fit independently, and their results are overlayed in orange and blue for region 1 and region 2, respectively.
Paper: A Deep Search for Exomoons around WISE 0855 with JWST
Authors: Mikayla J. Wilson, Mary Anne Limbach, Andrew J. Skemer, Johanna M. Vos, Brittany E. Miles, Melanie J. Rowland, Andrew Vanderburg, Adam C. Schneider, Caroline Morley, Brooke Kotten, Andrew Householder, Roxana Lupu, James Mang, Richard Freedman
Figure caption: Posterior parameter distributions of the GP + transit model results for the example injection described in Section 4.2 on the unflipped light curves. The transit specific parameters are the transit midtime, depth, duration, and slope. In this case, all but the transit slope are more constrained than the results of both the unflipped and flipped no-injection cases.
Paper: A Deep Search for Exomoons around WISE 0855 with JWST
Authors: Mikayla J. Wilson, Mary Anne Limbach, Andrew J. Skemer, Johanna M. Vos, Brittany E. Miles, Melanie J. Rowland, Andrew Vanderburg, Adam C. Schneider, Caroline Morley, Brooke Kotten, Andrew Householder, Roxana Lupu, James Mang, Richard Freedman
Figure caption: Posterior parameter distributions of the GP-only models for the no-injection case on the unflipped light curves. Both regions are fit independently, and their results are overlayed in orange and blue for region 1 and region 2, respectively.
Paper: A Deep Search for Exomoons around WISE 0855 with JWST
Authors: Mikayla J. Wilson, Mary Anne Limbach, Andrew J. Skemer, Johanna M. Vos, Brittany E. Miles, Melanie J. Rowland, Andrew Vanderburg, Adam C. Schneider, Caroline Morley, Brooke Kotten, Andrew Householder, Roxana Lupu, James Mang, Richard Freedman
Figure caption: Posterior parameter distributions of the GP-only models for the no-injection case on the flipped light curves. Both regions are fit independently, and their results are overlayed in orange and blue for region 1 and region 2, respectively.
Paper: A Deep Search for Exomoons around WISE 0855 with JWST
Authors: Mikayla J. Wilson, Mary Anne Limbach, Andrew J. Skemer, Johanna M. Vos, Brittany E. Miles, Melanie J. Rowland, Andrew Vanderburg, Adam C. Schneider, Caroline Morley, Brooke Kotten, Andrew Householder, Roxana Lupu, James Mang, Richard Freedman
Figure caption: Posterior parameter distributions of the GP + transit model results for the no-injection case on the flipped light curves.
Paper: Ultracool dwarf Science with MachIne LEarning (USMILE). I. Scalable Tree-based Models for Photometric Spectral Classification and New Discoveries from LSST Data Preview 1 and Euclid Quick Data Release 1
Authors: Zhoujian Zhang, 周健 张, Yanxia Li, 燕侠 李
Figure caption: Feature importance for the USMILE baseline classifier (left) and customized classifier (right), ranked by XGBoost F scores (i.e., the frequency with which each feature is used to split the data across all trees). The four most influential features for each classifier are highlighted in color.
Paper: Ultracool dwarf Science with MachIne LEarning (USMILE). I. Scalable Tree-based Models for Photometric Spectral Classification and New Discoveries from LSST Data Preview 1 and Euclid Quick Data Release 1
Authors: Zhoujian Zhang, 周健 张, Yanxia Li, 燕侠 李
Figure caption: ROC curves for the USMILE baseline classifier (left) and customized classifier (right). The corresponding values of the area under the curve (AUC) are summarized in Table 2. Open circles mark the points corresponding to the selected probability threshold for binary classification. In the left panel, the false-positive rate on the x-axis is shown on a logarithmic scale.
Paper: Ultracool dwarf Science with MachIne LEarning (USMILE). I. Scalable Tree-based Models for Photometric Spectral Classification and New Discoveries from LSST Data Preview 1 and Euclid Quick Data Release 1
Authors: Zhoujian Zhang, 周健 张, Yanxia Li, 燕侠 李
Figure caption: Learning curves of a representative USMILE customized classifier, showing cross-validation scores for the training set (blue) and testing set (red). These scores are computed using fivefold stratified cross-validation with shuffling to preserve class balance across folds. The two curves converge at large sample sizes near 0.91, demonstrating the model’s strong generalization performance with a well-balanced bias–variance trade-off.
Paper: Ultracool dwarf Science with MachIne LEarning (USMILE). I. Scalable Tree-based Models for Photometric Spectral Classification and New Discoveries from LSST Data Preview 1 and Euclid Quick Data Release 1
Authors: Zhoujian Zhang, 周健 张, Yanxia Li, 燕侠 李
Figure caption: The t-SNE projection of the complete labeled data set (Section 3.1). Positive (ultracool dwarfs) and negative (reddened early-type stars and quasars) samples are shown in blue and gray, respectively.
Paper: Ultracool dwarf Science with MachIne LEarning (USMILE). I. Scalable Tree-based Models for Photometric Spectral Classification and New Discoveries from LSST Data Preview 1 and Euclid Quick Data Release 1
Authors: Zhoujian Zhang, 周健 张, Yanxia Li, 燕侠 李
Figure caption: Photometric differences between Pan-STARRS1 and LSST in the i (top), z (middle), and y (bottom) bands, derived from synthetic model spectra of the SPHINX (green), Sonora Diamondback (orange), and Exo-REM (blue) grids. Each circle marks an individual synthetic spectrum from the respective model grid.
Paper: Ultracool dwarf Science with MachIne LEarning (USMILE). I. Scalable Tree-based Models for Photometric Spectral Classification and New Discoveries from LSST Data Preview 1 and Euclid Quick Data Release 1
Authors: Zhoujian Zhang, 周健 张, Yanxia Li, 燕侠 李
Figure caption: Visualization of the 30th decision tree from a USMILE customized classifier; each classifier contains 100 decision trees, with 400 classifiers trained in total. Each split evaluates a single color feature against a threshold, branching into two possible paths: a default direction (blue) and an alternative direction (red). Two example regions of the tree are shown, where “iy,” “zy,” “yj,” “yh,” “yk,” “yw1,” and “yw2” correspond to the iLSST − yLSST, zLSST − yLSST, yLSST − JVHS, yLSST − HVHS, yLSST − KS,VHS, yLSST − W1CatWISE, and yLSST − W2CatWISE colors, respectively. The resulting log-odds scores of the leaf nodes are then combined to yield the final classification probability.
Paper: Ultracool dwarf Science with MachIne LEarning (USMILE). I. Scalable Tree-based Models for Photometric Spectral Classification and New Discoveries from LSST Data Preview 1 and Euclid Quick Data Release 1
Authors: Zhoujian Zhang, 周健 张, Yanxia Li, 燕侠 李
Figure caption: Euclid NISP spectra for example candidates from the UCD-never category, which are classified as non-ultracool dwarfs by all 400 customized classifiers. Full NISP spectra are shown in black, with flux points from more than two dither positions (NDITH > 2) highlighted in blue. The spectra of best-fit templates (red) are overlaid for candidates whose ultracool nature is confirmed by our visual inspection. The top four spectra are confirmed as contaminants based on our template fits and visual inspection, consistent with the classifiers’ predictions. The second-to-last candidate is best matched to an M6 template, while the last is matched to an L2 template. The vertical white band marks the wavelength range (1.25–1.85 μm) used for template fitting analysis.
Paper: Ultracool dwarf Science with MachIne LEarning (USMILE). I. Scalable Tree-based Models for Photometric Spectral Classification and New Discoveries from LSST Data Preview 1 and Euclid Quick Data Release 1
Authors: Zhoujian Zhang, 周健 张, Yanxia Li, 燕侠 李
Figure caption: Top panel: the iLSST − yLSST and zLSST − yLSST colors for the positive (colored) and negative (gray) samples in our labeled data set, with positive samples color-coded by spectral types. The 15 ultracool dwarf discoveries confirmed with Euclid spectroscopy are shown as orange stars (Section 6). For objects lacking high-S/N Euclid spectra, we identify 25 high-quality candidates based on our USMILE models, shown as blue stars (Section 7). The remaining rejected candidates are shown as black circles. Bottom panel: the zLSST − yLSST and yLSST − HVHS colors for objects plotted in the top panel.
Paper: Ultracool dwarf Science with MachIne LEarning (USMILE). I. Scalable Tree-based Models for Photometric Spectral Classification and New Discoveries from LSST Data Preview 1 and Euclid Quick Data Release 1
Authors: Zhoujian Zhang, 周健 张, Yanxia Li, 燕侠 李
Figure caption: Coordinates of the initial candidates (Section 4) selected from LSST DP1, VHS, and CatWISE (gray circles) within the Extended Chandra Deep Field South (ECDFS; left), Euclid Deep Field South (EDF-S; middle), and a field at low Galactic latitude (Rubin SV 95 -25; right). Orange stars mark the ultracool dwarf discoveries confirmed by Euclid spectroscopy, all located in the EDF-S field (Section 6). These discoveries span spectral types M6–L2. Guided by the Euclid spectroscopic validation of the USMILE models, we further identify a set of high-quality M6–L9 candidates that lack high-S/N Euclid spectroscopy (Section 7), shown as blue stars.
Paper: Ultracool dwarf Science with MachIne LEarning (USMILE). I. Scalable Tree-based Models for Photometric Spectral Classification and New Discoveries from LSST Data Preview 1 and Euclid Quick Data Release 1
Authors: Zhoujian Zhang, 周健 张, Yanxia Li, 燕侠 李
Figure caption: The yLSST − JVHS and yLSST − W1CatWISE colors (top), as well as the yLSST − KS,VHS and yLSST − W2CatWISE colors (bottom), for the same types of object as plotted in Figure 11.
Paper: Ultracool dwarf Science with MachIne LEarning (USMILE). I. Scalable Tree-based Models for Photometric Spectral Classification and New Discoveries from LSST Data Preview 1 and Euclid Quick Data Release 1
Authors: Zhoujian Zhang, 周健 张, Yanxia Li, 燕侠 李
Figure caption: Linear correlations among the eight color features (left) and between each feature and the labels (right), where labels indicate whether a source is a genuine ultracool dwarf or a stellar/extragalactic contaminant. Pearson correlation coefficients are labeled.
Paper: Ultracool dwarf Science with MachIne LEarning (USMILE). I. Scalable Tree-based Models for Photometric Spectral Classification and New Discoveries from LSST Data Preview 1 and Euclid Quick Data Release 1
Authors: Zhoujian Zhang, 周健 张, Yanxia Li, 燕侠 李
Figure caption: Input vs. predicted spectral types from the USMILE regressor for the full testing set (black). Objects with four or more available color features are highlighted in red.
Paper: Ultracool dwarf Science with MachIne LEarning (USMILE). I. Scalable Tree-based Models for Photometric Spectral Classification and New Discoveries from LSST Data Preview 1 and Euclid Quick Data Release 1
Authors: Zhoujian Zhang, 周健 张, Yanxia Li, 燕侠 李
Figure caption: Feature importance of the USMILE regressor, in the same format as Figure 4.
Paper: Ultracool dwarf Science with MachIne LEarning (USMILE). I. Scalable Tree-based Models for Photometric Spectral Classification and New Discoveries from LSST Data Preview 1 and Euclid Quick Data Release 1
Authors: Zhoujian Zhang, 周健 张, Yanxia Li, 燕侠 李
Figure caption: Euclid NISP spectra for example candidates from the UCD-mixed category, which are classified as ultracool dwarfs by 1–199 out of 400 customized classifiers. The format follows that of Figure 13.
Paper: Ultracool dwarf Science with MachIne LEarning (USMILE). I. Scalable Tree-based Models for Photometric Spectral Classification and New Discoveries from LSST Data Preview 1 and Euclid Quick Data Release 1
Authors: Zhoujian Zhang, 周健 张, Yanxia Li, 燕侠 李
Figure caption: Euclid NISP spectra for example candidates from the UCD-agreed category, with the format following that of Figure 13. The spectrum of the last candidate, EUCL J035236.14−483012.4, only has a J-band S/N of 4.7. Although this S/N is lower than the threshold we adopt for performing template fitting analysis, we find its spectrum is similar to that of an L6 template. We keep this object as an ultracool dwarf candidate; high-quality spectroscopy is warranted to refine its spectral type and confirm its nature.
Paper: Removing Atmospheric Turbulence from Ground-based Astrometry with Fast Correlation Function Measurements
Authors: Daniel C. H. Gomes, Gary M. Bernstein, Claire-Alice Hébert
Figure caption: Distribution of rms astrometric residuals per axis on all 604 DES exposures (filled) and LSST simulations (step) under similar conditions. Red denotes the rms computed from the original correlation function of the displacements (pre-GPR); and green, the equivalent value obtained after subtraction of the GPR model (post-GPR).
Paper: Removing Atmospheric Turbulence from Ground-based Astrometry with Fast Correlation Function Measurements
Authors: Daniel C. H. Gomes, Gary M. Bernstein, Claire-Alice Hébert
Figure caption: Proper motion errors (in decl.) after 10 yr of LSST for a sample of 1000 randomly drawn sky positions and magnitudes, as a function of the i-band magnitude. Red points represent the scenario where no turbulence correction is applied (10 mas errors added in quadrature to the photon-noise error). Green points reduce the astrometric errors according to the performance of our turbulence reduction code (the astrometric floor is moved from 10 to 3 mas). Continuous lines are medians.
Paper: Removing Atmospheric Turbulence from Ground-based Astrometry with Fast Correlation Function Measurements
Authors: Daniel C. H. Gomes, Gary M. Bernstein, Claire-Alice Hébert
Figure caption: Improvement in the variance of the turbulence field on 604 DES exposures. The x-axis shows the pre-GPR variance computed from (12); and the y-axis, the variance of the GPR-model-subtracted residuals. Each point corresponds to a single DES exposure. The diagonal lines depict the ratios: 1x (No change), 5x reduction, 10x reduction and 20x reduction, which correspond to 1x, 2.2x, 3.2x and 4.5x reduction of the per-axis rms.
Paper: Removing Atmospheric Turbulence from Ground-based Astrometry with Fast Correlation Function Measurements
Authors: Daniel C. H. Gomes, Gary M. Bernstein, Claire-Alice Hébert
Figure caption: Total positional information expected after 10 yr of LSST, for a sample of 1000 Main Belt Asteroids. Red points denote the scenario where no turbulence correction is applied (10 mas errors added in quadrature to the photon-noise error). Green points reduce the astrometric errors according to the performance of our turbulence reduction code (the astrometric floor is moved from 10 to 3 mas). Continuous lines are medians.
Paper: Removing Atmospheric Turbulence from Ground-based Astrometry with Fast Correlation Function Measurements
Authors: Daniel C. H. Gomes, Gary M. Bernstein, Claire-Alice Hébert
Figure caption: Time elapsed for a single CPU core to perform the routine GPR in an entire DES exposure divided in 10 patches, including, for each patch, five-fold cross validation (80% of Gaia stars as reference) and a final run with all Gaia stars and the additional non-Gaia targets.
Paper: MEGA Mass Assembly with JWST: The MIRI EGS Galaxy and Active Galactic Nucleus Survey
Authors: Bren E. Backhaus, Allison Kirkpatrick, Guang Yang, Gregory Troiani, Kurt Hamblin, Jeyhan S. Kartaltepe, Dale D. Kocevski, Anton M. Koekemoer, Erini Lambrides, Casey Papovich, Kaila Ronayne
Figure caption: The combined integral number counts for MEGA’s four MIRI filters, with individual pointings shown in gray and the number counts using all pointings shown in green. The number counts of the total field have error bars representing the Poisson uncertainties. As a comparison, the CEERS number counts from G. Yang et al. (2023) are shown as blue crosses, while SMILES number counts from M. A. Stone et al. (2024) are purple triangles. The dashed vertical line represents the 5σ image depth for the MEGA pointings, which are shown in Table 1.
Paper: MEGA Mass Assembly with JWST: The MIRI EGS Galaxy and Active Galactic Nucleus Survey
Authors: Bren E. Backhaus, Allison Kirkpatrick, Guang Yang, Gregory Troiani, Kurt Hamblin, Jeyhan S. Kartaltepe, Dale D. Kocevski, Anton M. Koekemoer, Erini Lambrides, Casey Papovich, Kaila Ronayne
Figure caption: Completeness fraction as a function of flux for for each of the MEGA MIRI filters. 80% completeness for the F770W, F1000W, F1500W, and F2100W are 0.15, 0.40, 1.26, and 4.1, respectively. These 80% completeness measurements are comparable to the 5σ image depth.
Paper: MEGA Mass Assembly with JWST: The MIRI EGS Galaxy and Active Galactic Nucleus Survey
Authors: Bren E. Backhaus, Allison Kirkpatrick, Guang Yang, Gregory Troiani, Kurt Hamblin, Jeyhan S. Kartaltepe, Dale D. Kocevski, Anton M. Koekemoer, Erini Lambrides, Casey Papovich, Kaila Ronayne
Figure caption: The 15 × 15 pixel stacked on-the-sky distribution of 186 compact and isolated galaxies in the F770W filter used to model the injected fake sources to determine source completeness.
Paper: MEGA Mass Assembly with JWST: The MIRI EGS Galaxy and Active Galactic Nucleus Survey
Authors: Bren E. Backhaus, Allison Kirkpatrick, Guang Yang, Gregory Troiani, Kurt Hamblin, Jeyhan S. Kartaltepe, Dale D. Kocevski, Anton M. Koekemoer, Erini Lambrides, Casey Papovich, Kaila Ronayne
Figure caption: Distribution of F770W photometric fluxes for our MEGA survey sample compared to 5σ CEERS detections. The vertical dashed line represents our 5σ image depth for the F770W filter.
Paper: Exploring the Mass–Metallicity Relationships with the DESI BGS and ELG Samples
Authors: Yunxiao Jia, Hu Zou, Ali Luo, Wen Hou, Shuguo Ma, Jipeng Sui
Figure caption: Redshift distributions of the BGS (top panel) and ELGs (bottom panel), showing the number of sources as a function of redshift.
Paper: MOA-2010-BLG-328: Keck and HST Expose the Limits of Occams Razor in Microlensing
Authors: Aikaterini Vandorou, David P. Bennett, Jean-Philippe Beaulieu, Aparna Bhattacharya, Joshua W. Blackman, Ian A. Bond, Andrew A. Cole, Naoki Koshimoto, Clément Ranc, Natalia E. Rektsini, Sean K. Terry
Figure caption: Mass–distance relations for MB10328, obtained using the Einstein ring radius, θE in blue and the Keck K-band flux constraint from mass–luminosity relations in red. Panel (A) shows the full MCMC probability distribution with the “large” πE constraint in orange and the “small” πE constraint in green. Panels (B) and (C) show the constraints for the “large” and “small” πE values, along with the corresponding θE and K-band flux constraints.
Paper: MOA-2010-BLG-328: Keck and HST Expose the Limits of Occams Razor in Microlensing
Authors: Aikaterini Vandorou, David P. Bennett, Jean-Philippe Beaulieu, Aparna Bhattacharya, Joshua W. Blackman, Ian A. Bond, Andrew A. Cole, Naoki Koshimoto, Clément Ranc, Natalia E. Rektsini, Sean K. Terry
Figure caption: A zoom of the entry of the planetary anomaly for the two best-fit models, small πE in orange and large πE in green, and the corresponding MOA and OGLE data depicted by a circle and diamond, respectively.
Paper: Direct Spectroscopy of 51 Eridani b with JWST NIRSpec
Authors: Alexander Madurowicz, Jean-Baptiste Ruffio, Bruce Macintosh, Marshall Perrin, Quinn M. Konopacky, Aneesh Baburaj, Kielan Hoch
Figure caption: Diagnostic plots generated during data reduction. (Left) Detection significance as a function of the node spacing hyperparameter for each individual data sequence showcasing the robustness of the detection, as well as the optimal node spacing. (Right) Best-fit coordinates in slit A1 (top) and slit A2 (bottom) and corresponding regularized polynomial fit as a function of wavelength computed during the centroiding/coordinate correction step.
Paper: Direct Spectroscopy of 51 Eridani b with JWST NIRSpec
Authors: Alexander Madurowicz, Jean-Baptiste Ruffio, Bruce Macintosh, Marshall Perrin, Quinn M. Konopacky, Aneesh Baburaj, Kielan Hoch
Figure caption: Cross-correlation function calculated on individual data sets, showing detections in slits A1 and A2 individually. (Left) 2D CCF detection maps in IFU Y–RV phase space, (middle) CCF slice through RV = 0 km s−1, and (right) CCF slice through IFU Y = −0﹩\mathop{.}\limits^{\unicode{x02033}}﹩04, the expected position of the planet.
Paper: Metallicity Regulates Planet Formation across All Masses
Authors: Max Nguyen, Vardan Adibekyan
Figure caption: Z distributions of planet-host and nonhost stars. Left: distributions based on individual planet detections, with hosts grouped by the mass of any detected planet in the system. Right: same as the left, but hosts are grouped according to the mass of the most massive planet in each system. The vertical dashed lines indicate the mean Z of each distribution. The nonhost sample is shown in gray for comparison.
Paper: Metallicity Regulates Planet Formation across All Masses
Authors: Max Nguyen, Vardan Adibekyan
Figure caption: Difference between primordial and present-day Z as a function of stellar age (crosses) for the sample of 30 FGK stars from V. Adibekyan et al. (2024). The red error bars denote the uncertainties in the Z differences.
Paper: Metallicity Regulates Planet Formation across All Masses
Authors: Max Nguyen, Vardan Adibekyan
Figure caption: Distribution of stars in the Kiel diagram. Gray points represent the nonhost sample from Hypatia, blue points indicate the HARPS nonhost stars, and red points correspond to the planet-hosting stars from our sample.