Hot subdwarf binaries J/A+A/691/A223 ivo://CDS.VizieR/J/A+A/691/A223 doi:10.26093/cds/vizier.36910223 CDS Viscasillas Vazquez C. Solano E. Ulla A. Ambrosch M. Alvarez M.A.,Manteiga M. Magrini L. Santovena-Gomez R. Dafonte C. Perez-Fernandez E.,Aller A. Drazdauskas A. Mikolaitis S. Rodrigo C. 2025-02-18T20:02:01Z 2024-11-15T09:58:06Z CDS support team CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France cds-question@unistra.fr subdwarf-stars visible-astronomy Hot subdwarf stars are compact blue evolved objects, that are located by the blue end of the Horizontal Branch. Most models agree on a common envelope binary evolution scenario in the Red Giant phase. However, the current binarity rate for these objects is yet unsolved.This study aims to develop a novel classification method for identifying hot subdwarf binaries using Artificial Intelligence techniques and data from the third Gaia data release (GDR3). The methods used for hot subdwarf binary classification include supervised and unsupervised machine learning techniques. Specifically, we have used Support Vector Machines (SVM) to classify 3084 hot subdwarf stars based on their colour-magnitude properties. Among these, 2815 objects have Gaia DR3 BP/RP spectra, which were classified using Self-Organizing Maps (SOM) and Convolutional Neural Networks (CNN). Additional analysis onto a golden sample of 88 well-defined objects, is also presented. The findings demonstrate a high agreement level (~70-90%) with the classifications from the Virtual Observatory Sed Analyzer (VOSA) tool. SVM in a radial basis function achieves 70.97% reproducibility for binary targets using photometry, and CNN reaches 84.94% for binary detection using spectroscopy. We also find that the single-binary differences are especially observable on the infrared flux in our Gaia DR3 BP/BR spectra, at wavelengths larger than ~700nm. We find that all the methods used are in fairly good agreement and are particularly effective to discern between single and binary systems. The agreement is also consistent with the results previously obtained with VOSA. In global terms, considering all quality metrics, CNN is the method that provides the best accuracy. The methods also appear effective for detecting peculiarities in the spectra. While promising, challenges in dealing with uncertain compositions highlight the need for caution, suggesting further research is needed to refine techniques and enhance automated classification reliability, particularly for large-scale surveys. 2024A&A...691A.223V https://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/691/A223 Catalog Research IsServedBy TAP VizieR generic service IsServedBy Conesearch service related-to I/355 : Gaia DR3 Part 1. Main source (Gaia Collaboration, 2022) https://cds.unistra.fr/vizier-org/licences_vizier.html https://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/A+A/691/A223 https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/A+A/691/A223 http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/A+A/691/A223 https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/A+A/691/A223 https://vizier.iucaa.in/viz-bin/votable?-source=J/A+A/691/A223 http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/A+A/691/A223 GET text/xml+votable https://tapvizier.cds.unistra.fr/TAPVizieR/tap Cone search capability for table J/A+A/691/A223/tablea1 (Classification labels for the different methods) https://vizier.cds.unistra.fr/viz-bin/conesearch/J/A+A/691/A223/tablea1? https://vizier.iucaa.in/viz-bin/conesearch/J/A+A/691/A223/tablea1? http://vizieridia.saao.ac.za/viz-bin/conesearch/J/A+A/691/A223/tablea1? 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48919 48922 48928 48931 48933 48935 48940 48944 48946 48953 48956 48961 49020 49038 49042 49049 49059 49061 49093 49122 49130 https://cdsarc.cds.unistra.fr/viz-bin/moc/J/A+A/691/A223?format=ascii Optical default J/A+A/691/A223/tablea1 Classification labels for the different methods Flag Number of agreeing labels from different methods meta.number int Simbad Simbad column added by the CDS meta.ref.url char _RA Right Ascension (J2000) from SIMBAD (not part of the original data) deg pos.eq.ra;meta.main _DE Declination (J2000) from SIMBAD (not part of the original data) deg pos.eq.dec;meta.main recno Record number assigned by the VizieR team. Should Not be used for identification. meta.record int GaiaDR3 Gaia DR3 identifier meta.id Object Object identifier meta.id;meta.main char VOSA [0/1] Label from VOSA meta.code int w-SVM-linear [0/1] Label from weighted SVM with linear basis function meta.code int w-SVM-rbf [0/1] Label from weighted SVM with radial basis function meta.code int SOM [0/1] Label from SOM meta.code int CNN [0/1] Label from CNN meta.code int Pbinary-SOM [0/1] Probability that binary from SOM stat.fit.goodness float Pbinary-CNN [0/1] Probability that binary from CNN stat.fit.goodness float