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Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques

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dc.contributor.authorSirunyan, A. M.
dc.contributor.authorTumasyan, A.
dc.contributor.authorAdam, W.
dc.contributor.authorAmbrogi, F.
dc.contributor.authorBychkova, O.
dc.contributor.authorChadeeva, M.
dc.contributor.authorParygin, P.
dc.contributor.authorPopova, E.
dc.contributor.authorRusinov, V.
dc.contributor.authorMatveev, V.
dc.contributor.authorЧадеева, Марина Валентиновна
dc.contributor.authorРусинов, Владимир Юрьевич
dc.contributor.authorМатвеев, Виктор Анатольевич
dc.date.accessioned2024-11-26T15:18:29Z
dc.date.available2024-11-26T15:18:29Z
dc.date.issued2020
dc.description.abstract© 2020 CERN for the benefit of the CMS collaboration..Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at s = 13TeV, corresponding to an integrated luminosity of 35.9 fb-1. Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency.
dc.identifier.citationIdentification of heavy, energetic, hadronically decaying particles using machine-learning techniques / Sirunyan, A.M. [et al.] // Journal of Instrumentation. - 2020. - 15. - № 6. - 10.1088/1748-0221/15/06/P06005
dc.identifier.doi10.1088/1748-0221/15/06/P06005
dc.identifier.urihttps://www.doi.org/10.1088/1748-0221/15/06/P06005
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85088524436&origin=resultslist
dc.identifier.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000545350900005
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/22087
dc.relation.ispartofJournal of Instrumentation
dc.titleIdentification of heavy, energetic, hadronically decaying particles using machine-learning techniques
dc.typeArticle
dspace.entity.typePublication
oaire.citation.issue6
oaire.citation.volume15
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