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Boosted decision trees approach to neck alpha events discrimination in DEAP-3600 experiment

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dc.contributor.authorGrobov, A.
dc.contributor.authorIlyasov, A.
dc.contributor.authorИльясов, Айдар Иршатович
dc.date.accessioned2024-11-26T13:47:33Z
dc.date.available2024-11-26T13:47:33Z
dc.date.issued2020
dc.description.abstract© 2020 IOP Publishing Ltd.Machine learning (ML) has been widely applied in high energy physics to help the physical community in particle classification and data analysis. Here we describe the application of machine learning to solve the problem of classifying background and signal events for the DEAP-3600 dark matter search experiment (SNOLAB, Canada). We apply Boosted Decision Trees (BDT) algorithm of ML with improvements from Extra Trees and eXtra Gradient Boosting (XGBoost) methods [1, 2].
dc.identifier.citationGrobov, A. Boosted decision trees approach to neck alpha events discrimination in DEAP-3600 experiment / Grobov, A., Ilyasov, A. // Physica Scripta. - 2020. - 95. - № 7. - 10.1088/1402-4896/ab8dff
dc.identifier.doi10.1088/1402-4896/ab8dff
dc.identifier.urihttps://www.doi.org/10.1088/1402-4896/ab8dff
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85085582592&origin=resultslist
dc.identifier.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000536264900001
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/21843
dc.relation.ispartofPhysica Scripta
dc.titleBoosted decision trees approach to neck alpha events discrimination in DEAP-3600 experiment
dc.typeArticle
dspace.entity.typePublication
oaire.citation.issue7
oaire.citation.volume95
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