<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-04-17T03:23:22Z</responseDate><request verb="GetRecord" identifier="oai:www.recercat.cat:2117/174260" metadataPrefix="didl">https://recercat.cat/oai/request</request><GetRecord><record><header><identifier>oai:recercat.cat:2117/174260</identifier><datestamp>2026-01-28T02:33:44Z</datestamp><setSpec>com_2072_1033</setSpec><setSpec>col_2072_452950</setSpec></header><metadata><d:DIDL xmlns:d="urn:mpeg:mpeg21:2002:02-DIDL-NS" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="urn:mpeg:mpeg21:2002:02-DIDL-NS http://standards.iso.org/ittf/PubliclyAvailableStandards/MPEG-21_schema_files/did/didl.xsd">
   <d:Item id="hdl_2117_174260">
      <d:Descriptor>
         <d:Statement mimeType="application/xml; charset=utf-8">
            <dii:Identifier xmlns:dii="urn:mpeg:mpeg21:2002:01-DII-NS" xsi:schemaLocation="urn:mpeg:mpeg21:2002:01-DII-NS http://standards.iso.org/ittf/PubliclyAvailableStandards/MPEG-21_schema_files/dii/dii.xsd">urn:hdl:2117/174260</dii:Identifier>
         </d:Statement>
      </d:Descriptor>
      <d:Descriptor>
         <d:Statement mimeType="application/xml; charset=utf-8">
            <oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
               <dc:title>Random Forest identification of the thin disc, thick disc, and halo Gaia-DR2 white dwarf population</dc:title>
               <dc:creator>Torres Gil, Santiago</dc:creator>
               <dc:creator>Cantero, C.</dc:creator>
               <dc:creator>Rebassa Mansergas, Alberto</dc:creator>
               <dc:creator>Skorobogatov, G.</dc:creator>
               <dc:creator>Jiménez Esteban, F. M.</dc:creator>
               <dc:creator>Solano Márquez, Enrique</dc:creator>
               <dc:subject>Àrees temàtiques de la UPC::Física::Astronomia i astrofísica</dc:subject>
               <dc:subject>White dwarf stars</dc:subject>
               <dc:subject>Stars--Luminosity function</dc:subject>
               <dc:subject>Stars--Masses</dc:subject>
               <dc:subject>Stars: white dwarfs</dc:subject>
               <dc:subject>Galaxy: stellar content</dc:subject>
               <dc:subject>Stars: luminosity function</dc:subject>
               <dc:subject>mass function</dc:subject>
               <dc:subject>Estels nans</dc:subject>
               <dc:subject>Galàxies -- Formació</dc:subject>
               <dc:description>Gaia-DR2 has provided an unprecedented number of white dwarf candidates of ourGalaxy. In particular, it is estimated thatGaia-DR2 has observed nearly 400 000 ofthese objects and close to 18 000 up to 100 pc from the Sun. This large quantity ofdata requires a thorough analysis in order to uncover their main Galactic popula-tion properties, in particular the thin and thick disk and halo components. Takingadvantage of recent developments in artificial intelligence techniques, we make useof a detailed Random Forest algorithm to analyse an 8-dimensional space (equato-rial coordinates, parallax, proper motion components and photometric magnitudes)of accurate data provided byGaia-DR2 within 100 pc from the Sun. With the aid ofa thorough and robust population synthesis code we simulated the different compo-nents of the Galactic white dwarf population to optimize the information extractedfrom the algorithm for disentangling the different population components. The algo-rithm is first tested in a known simulated sample achieving an accuracyof 85.3%. Ourmethodology is thoroughly compared to standard methods based on kinematic criteriademonstrating that our algorithm substantially improves previous approaches. Oncetrained, the algorithm is then applied to theGaia-DR2 100 pc white dwarf sample,identifying 12 227 thin disk, 1410 thick disk and 95 halo white dwarf candidates, whichrepresent a proportion of 74:25:1, respectively. Hence, the numerical spatial densitiesare (3.6±0.4)×10-3pc-3, (1.2±0.4)×10-3pc-3and (4.8±0.4)×10-5pc-3forthe thin disk, thick disk and halo components, respectively. The populations thus ob-tained represent the most complete and volume-limited samples to date of the differentcomponents of the Galactic white dwarf population.</dc:description>
               <dc:description>Peer Reviewed</dc:description>
               <dc:description>Preprint</dc:description>
               <dc:date>2019-03-13</dc:date>
               <dc:type>Article</dc:type>
               <dc:relation>This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society Published by Oxford University Press on behalf of the Royal Astronomical Society.</dc:relation>
               <dc:relation>https://academic.oup.com/mnras/article-abstract/485/4/5573/5398538</dc:relation>
               <dc:relation>info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AYA2017-86274-P/ES/DEL ENFRIAMIENTO A LAS EXPLOSIONES: LA FISICA DE LOS OBJETOS COMPACTOS/</dc:relation>
               <dc:relation>info:eu-repo/grantAgreement/SPAIN/MINECO/RYC-2016-20254</dc:relation>
               <dc:relation>info:eu-repo/grantAgreement/AGAUR/SGR-661/201</dc:relation>
               <dc:relation>info:eu-repo/grantAgreement/EC/H2020/653477/EU/Astronomy ESFRI and Research Infrastructure Cluster/ASTERICS</dc:relation>
               <dc:rights>http://creativecommons.org/licenses/by-nc-nd/3.0/es/</dc:rights>
               <dc:rights>Open Access</dc:rights>
               <dc:rights>Attribution-NonCommercial-NoDerivs 3.0 Spain</dc:rights>
               <dc:publisher>Oxford University Press</dc:publisher>
            </oai_dc:dc>
         </d:Statement>
      </d:Descriptor>
   </d:Item>
</d:DIDL></metadata></record></GetRecord></OAI-PMH>