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<ri:Resource created="2017-06-27T10:15:26Z" 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>Classification of 2XMM variable sources</title><shortName>J/ApJ/786/20</shortName><identifier>ivo://CDS.VizieR/J/ApJ/786/20</identifier><altIdentifier>doi:10.26093/cds/vizier.17860020</altIdentifier><curation><publisher ivo-id="ivo://CDS">CDS</publisher><creator><name>Lo K.K.</name></creator><creator><name>Farrell S.</name></creator><creator><name>Murphy T.</name></creator><creator><name>Gaensler B.M.</name></creator><date role="Updated">2017-09-04T07:58:17Z</date><date role="Created">2017-06-27T10:15:26Z</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>x-ray-sources</subject><subject>galaxy-classification-systems</subject><description>To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ~97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7-500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.</description><source format="bibcode">2014ApJ...786...20L</source><referenceURL>https://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJ/786/20</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/IX/40">IX/40 : The XMM-Newton 2nd Incremental Source Catalogue (2XMMi)</relatedResource><relatedResource ivo-id="ivo://CDS.VizieR/J/A+A/493/339">J/A+A/493/339 : XMM-Newton serendipitous Survey. V. (Watson+, 2009)</relatedResource><relatedResource ivo-id="ivo://CDS.VizieR/J/MNRAS/416/1844">J/MNRAS/416/1844 : 2XMM ultraluminous X-ray source candidates (Walton+, 2011)</relatedResource></relationship></content><rights>https://cds.unistra.fr/vizier-org/licences_vizier.html</rights><capability><interface xsi:type="vr:WebBrowser"><accessURL use="full">https://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/ApJ/786/20</accessURL><mirrorURL title="VizieR at IUCAA: Pune, India">https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/ApJ/786/20</mirrorURL><mirrorURL title="VizieR at SAAO: SAAO, South Africa">http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/ApJ/786/20</mirrorURL></interface></capability><capability><interface xsi:type="vs:ParamHTTP"><accessURL use="base">https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/ApJ/786/20</accessURL><mirrorURL title="VizieR at IUCAA: Pune, India">https://vizier.iucaa.in/viz-bin/votable?-source=J/ApJ/786/20</mirrorURL><mirrorURL 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