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

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Sirunyan_2020_J._Inst._15_P06005.pdf(5.27 MB)
Дата
2020
Авторы
Sirunyan, A. M.
Tumasyan, A.
Adam, W.
Ambrogi, F.
Bychkova, O.
Chadeeva, M.
Parygin, P.
Popova, E.
Rusinov, V.
Matveev, V.
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Организационная единица
Институт ядерной физики и технологий
Цель ИЯФиТ и стратегия развития - создание и развитие научно-образовательного центра мирового уровня в области ядерной физики и технологий, радиационного материаловедения, физики элементарных частиц, астрофизики и космофизики.
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Аннотация
© 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.
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Identification 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
URI
https://www.doi.org/10.1088/1748-0221/15/06/P06005
 https://www.scopus.com/record/display.uri?eid=2-s2.0-85088524436&origin=resultslist
 http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000545350900005
 https://openrepository.mephi.ru/handle/123456789/22087
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