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<ri:Resource created="2025-01-22T07:38:40Z" status="active" updated="2025-05-19T09:50:00Z" version="1.2" xmlns:cs="http://www.ivoa.net/xml/ConeSearch/v1.0" xmlns:ri="http://www.ivoa.net/xml/RegistryInterface/v1.0" xmlns:vr="http://www.ivoa.net/xml/VOResource/v1.0" xmlns:vs="http://www.ivoa.net/xml/VODataService/v1.1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.ivoa.net/xml/ConeSearch/v1.0 http://vo.ari.uni-heidelberg.de/docs/schemata/ConeSearch.xsd http://www.ivoa.net/xml/VOResource/v1.0 http://vo.ari.uni-heidelberg.de/docs/schemata/VOResource.xsd http://www.ivoa.net/xml/VODataService/v1.1 http://vo.ari.uni-heidelberg.de/docs/schemata/VODataService.xsd" xsi:type="vs:CatalogService"><title>Catalog of hot subdwarf candidates</title><shortName>J/A+A/693/A245</shortName><identifier>ivo://CDS.VizieR/J/A+A/693/A245</identifier><altIdentifier>doi:10.26093/cds/vizier.36930245</altIdentifier><curation><publisher ivo-id="ivo://CDS">CDS</publisher><creator><name>Wu H.</name></creator><creator><name>Bu Y.</name></creator><creator><name>Zhang J.</name></creator><creator><name>Zhang M.</name></creator><creator><name>Yi Z.</name></creator><creator><name>Liu M.</name></creator><creator><name>Kong X.</name></creator><creator><name>Lei Z.</name></creator><date role="Updated">2025-03-03T20:01:23Z</date><date role="Created">2025-01-22T07:38:40Z</date><contact><name>CDS support team</name><address>CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France</address><email>cds-question@unistra.fr</email></contact></curation><content><subject>subdwarf-stars</subject><subject>visible-astronomy</subject><subject>sloan-photometry</subject><description>Hot subdwarfs are essential for understanding the structure and evolution of low-mass stars, binary systems, astroseismology, and atmospheric diffusion processes. In recent years, deep learning has shown significant progress in hot subdwarf searches. However, most approaches tend to focus on modeling with spectral data, which are inherently more costly and scarce compared to photometric data. To maximize the reliable candidates, this paper utilized Sloan Digital Sky Survey (SDSS) photometric images to construct a two-stage hot subdwarfs search model called SwinBayesNet, which combines the Swin Transformer and Bayesian Neural Networks. This model not only provides classification results but also estimates uncertainty. Five classes of stars prone to confusion with hot subdwarfs, including O-type stars, B-type stars, A-type stars, white dwarfs (WDs), and blue horizontal branch stars (BHBs), were selected as negative examples for the model. On the test set, the two-stage model achieved F1 scores of 0.90 and 0.89 in the two-class and three-class classification stages, respectively. Subsequently, with the help of Gaia DR3, a large-scale candidate search was conducted in SDSS DR17. We found 6804 hot subdwarf candidates, including 601 new discoveries. Based on this, we applied a model threshold of 0.95 and Bayesian uncertainty estimation for further screening, refining the candidates to 3413 high-confidence samples, which included 331 new discoveries.</description><source format="bibcode">2025A&amp;A...693A.245W</source><referenceURL>https://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/693/A245</referenceURL><type>Catalog</type><contentLevel>Research</contentLevel><relationship><relationshipType>IsServedBy</relationshipType><relatedResource ivo-id="ivo://CDS.VizieR/TAP">TAP VizieR generic service</relatedResource></relationship><relationship><relationshipType>IsServedBy</relationshipType><relatedResource>Conesearch service</relatedResource></relationship><relationship><relationshipType>related-to</relationshipType><relatedResource ivo-id="ivo://CDS.VizieR/I/355">I/355 : Gaia DR3 Part 1. 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ivo-id="ivo://ivoa.net/std/moc">https://cdsarc.cds.unistra.fr/viz-bin/moc/J/A+A/693/A245?format=ascii</footprint><waveband>Optical</waveband></coverage><tableset><schema><name>default</name><table><name>J/A+A/693/A245/table9</name><description>6804 Catalog of hot subdwarf candidates</description><column><name>2episnorm</name><description>Two-class classification epistemic uncertainty (normalised) (Two_epistemic_norm)</description><ucd>stat.error</ucd><dataType xsi:type="vs:VOTableType">double</dataType></column><column><name>2aleanorm</name><description>Two-class classification aleatoric uncertainty (normalised) (Two_aleatoric_norm)</description><ucd>stat.error</ucd><dataType xsi:type="vs:VOTableType">double</dataType></column><column><name>2epissoft</name><description>Two-class classification epistemic uncertainty (soft) (Two_epistemic_soft)</description><ucd>stat.error</ucd><dataType xsi:type="vs:VOTableType">double</dataType></column><column><name>2aleasoft</name><description>Two-class classification aleatoric uncertainty (soft) (Two_aleatoric_soft)</description><ucd>stat.error</ucd><dataType xsi:type="vs:VOTableType">double</dataType></column><column><name>2rel</name><description>Two-class classification reliability (Two_reliability)</description><ucd>meta.code.qual</ucd><dataType xsi:type="vs:VOTableType" arraysize="3*">char</dataType></column><column><name>3prob</name><description>Three-class classification predicted probability (Three_probability)</description><ucd>stat.fit.goodness</ucd><dataType xsi:type="vs:VOTableType">double</dataType></column><column><name>3episnorm</name><description>Three-class classification Epistemic uncertainty (normalised) (Three_epistemic_norm)</description><ucd>stat.error</ucd><dataType xsi:type="vs:VOTableType">double</dataType></column><column><name>3aleanorm</name><description>Three-class classification aleatoric uncertainty (normalised) (Three_aleatoric_norm)</description><ucd>stat.error</ucd><dataType xsi:type="vs:VOTableType">double</dataType></column><column><name>3epissoft</name><description>Three-class classification epistemic uncertainty (soft) (Three_epistemic_soft)</description><ucd>stat.error</ucd><dataType xsi:type="vs:VOTableType">double</dataType></column><column><name>3aleasoft</name><description>Three-class classification aleatoric uncertainty (soft) (Three_aleatoric_soft)</description><ucd>stat.error</ucd><dataType xsi:type="vs:VOTableType">double</dataType></column><column><name>3rel</name><description>Three-class classification Reliability (Three_reliability)</description><ucd>meta.code.qual</ucd><dataType xsi:type="vs:VOTableType" arraysize="3*">char</dataType></column><column><name>Source</name><description>The source of candidates, Culpan or New (Source)</description><ucd>meta.note</ucd><dataType xsi:type="vs:VOTableType" arraysize="6*">char</dataType></column><column><name>Highconf</name><description>[Yes No] Confidence status of candidates (High_confidence)</description><ucd>meta.code.status;stat.fit.param</ucd><dataType xsi:type="vs:VOTableType" arraysize="3*">char</dataType></column><column><name>recno</name><description>Record number assigned by the VizieR team. Should Not be used for identification.</description><ucd>meta.record</ucd><dataType xsi:type="vs:VOTableType">int</dataType></column><column><name>RAJ2000</name><description>Right Ascension (J2000) (RA)</description><unit>deg</unit><ucd>pos.eq.ra;meta.main</ucd></column><column><name>DEJ2000</name><description>Declination (J2000) (DEC)</description><unit>deg</unit><ucd>pos.eq.dec;meta.main</ucd></column><column><name>SDSS</name><description>objID in SDSS DR17 (SDSSDR17_objID)</description><ucd>meta.id;meta.main</ucd><dataType xsi:type="vs:VOTableType" arraysize="19*">char</dataType></column><column><name>GaiaDR3</name><description>source_id in Gaia DR3 (GaiaDR3_source_id)</description><ucd>meta.id</ucd><dataType xsi:type="vs:VOTableType" arraysize="19*">char</dataType></column><column><name>2prob</name><description>Two-class classification predicted probability (Two_probability)</description><ucd>stat.fit.goodness</ucd><dataType xsi:type="vs:VOTableType">double</dataType></column></table></schema></tableset></ri:Resource>