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<ri:Resource created="2022-09-09T08:59:34Z" 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>Hot subdwarf candidates from LAMOST DR7</title><shortName>J/ApJS/259/5</shortName><identifier>ivo://CDS.VizieR/J/ApJS/259/5</identifier><altIdentifier>doi:10.26093/cds/vizier.22590005</altIdentifier><curation><publisher ivo-id="ivo://CDS">CDS</publisher><creator><name>Tan L.</name></creator><creator><name>Mei Y.</name></creator><creator><name>Liu Z.</name></creator><creator><name>Luo Y.</name></creator><creator><name>Deng H.</name></creator><creator><name>Wang F.</name></creator><creator><name>Deng L.</name></creator><creator><name>Liu C.</name></creator><date role="Updated">2023-11-13T08:06:41Z</date><date role="Created">2022-09-09T08:59:34Z</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>spectroscopy</subject><description>Hot subdwarf stars are a particular type of star that is crucial for studying binary evolution and atmospheric diffusion processes. In recent years, identifying hot subdwarfs by machine-learning methods has become a hot topic, but there are still limitations in automation and accuracy. In this paper, we proposed a robust identification method based on a convolutional neural network. We first constructed the data set using the spectral data of LAMOST DR7-V1. We then constructed a hybrid recognition model including an eight-class classification model and a binary classification model. The model achieved an accuracy of 96.17% on the testing set. To further validate the accuracy of the model, we selected 835 hot subdwarfs that were not involved in the training process from the identified LAMOST catalog (2428, including repeated observations) as the validation set. An accuracy of 96.05% was achieved. On this basis, we used the model to filter and classify all 10,640,255 spectra of LAMOST DR7-V1, and obtained a catalog of 2393 hot subdwarf candidates, of which 2067 have been confirmed. We found 25 new hot subdwarfs among the remaining candidates by manual validation. The overall accuracy of the model is 87.42%. Overall, the model presented in this study can effectively identify specific spectra with robust results and high accuracy, and can be further applied to the classification of large-scale spectra and the search for specific targets.</description><source format="bibcode">2022ApJS..259....5T</source><referenceURL>https://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJS/259/5</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/V/146">V/146 : LAMOST DR1 catalogs (Luo+, 2015)</relatedResource><relatedResource ivo-id="ivo://CDS.VizieR/V/156">V/156 : LAMOST DR7 catalogs (Luo+, 2019)</relatedResource><relatedResource ivo-id="ivo://CDS.VizieR/J/MNRAS/380/1098">J/MNRAS/380/1098 : UV-upturn of elliptical galaxies model (Han+, 2007)</relatedResource><relatedResource ivo-id="ivo://CDS.VizieR/J/A+A/549/A110">J/A+A/549/A110 : Metal abundances of sdB stars (Geier, 2013)</relatedResource><relatedResource ivo-id="ivo://CDS.VizieR/J/ApJ/764/25">J/ApJ/764/25 : FUSE spectra analysis of hot subdwarf stars (Jenkins, 2013)</relatedResource><relatedResource ivo-id="ivo://CDS.VizieR/J/ApJ/818/202">J/ApJ/818/202 : Hot subdwarf stars in LAMOST DR1 (Luo+, 2016)</relatedResource><relatedResource ivo-id="ivo://CDS.VizieR/J/A+A/600/A50">J/A+A/600/A50 : Catalog of hot subdwarf stars (Geier+, 2017)</relatedResource><relatedResource ivo-id="ivo://CDS.VizieR/J/ApJ/868/70">J/ApJ/868/70 : Hot subdwarf stars from Gaia DR2 and LAMOST DR5 (Lei+, 2018)</relatedResource><relatedResource ivo-id="ivo://CDS.VizieR/J/ApJ/881/135">J/ApJ/881/135 : Hot subdwarf stars from GaiaDR2 &amp; LAMOST DR5. II. (Lei+, 2019)</relatedResource><relatedResource ivo-id="ivo://CDS.VizieR/J/A+A/621/A38">J/A+A/621/A38 : Gaia catalogue of hot subluminous stars (Geier+, 2019)</relatedResource><relatedResource ivo-id="ivo://CDS.VizieR/J/ApJ/898/64">J/ApJ/898/64 : Hot subdwarf stars from Gaia &amp; LAMOST. II. RVs (Luo+, 2020)</relatedResource><relatedResource ivo-id="ivo://CDS.VizieR/J/ApJ/889/117">J/ApJ/889/117 : Hot subdwarf stars from GaiaDR2 &amp; LAMOSTDR6+7. I. 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Should Not be used for identification.</description><ucd>meta.record</ucd><dataType xsi:type="vs:VOTableType">int</dataType></column><column><name>LAMOST</name><description>LAMOST designation (JHHMMSS.ss+DDMMSS.s)</description><ucd>meta.id;meta.main</ucd><dataType xsi:type="vs:VOTableType" arraysize="20*">char</dataType></column><column><name>ObsID</name><description>[106081/746810176] Unique number ID of this spectrum</description><ucd>meta.id</ucd><dataType xsi:type="vs:VOTableType">long</dataType></column><column><name>RAdeg</name><description>Right Ascension in decimal degrees (J2000)</description><unit>deg</unit><ucd>pos.eq.ra</ucd></column><column><name>DEdeg</name><description>[-7.14/82] Declination decimal degrees (J2000)</description><unit>deg</unit><ucd>pos.eq.dec</ucd></column><column><name>SNRg</name><description>[2.18/629.1] Signal-to-Noise in g band</description><ucd>stat.snr</ucd><dataType xsi:type="vs:VOTableType">float</dataType></column><column><name>SNRu</name><description>[0/436] Signal-to-Noise in u band</description><ucd>stat.snr</ucd><dataType xsi:type="vs:VOTableType">float</dataType></column><column><name>SNRz</name><description>[0/411.3]? 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