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

CMS Collaboration; Sirunyan, A.M.; Tumasyan, A.; Adam, W.; Ambrogi, F.; Bergauer, T.; Dragicevic, M.; Erö, J.; Valle, A. Escalante Del; Flechl, M.; Frühwirth, R.; Jeitler, M.; Krammer, N.; Krätschmer, I.; Liko, D.; Madlener, T.; Mikulec, I.; Rad, N.; Schieck, J.; Schöfbeck, R.; Spanring, M.; ... mehr

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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Verlagsausgabe §
DOI: 10.5445/IR/1000122256
Veröffentlicht am 29.07.2020
DOI: 10.1088/1748-0221/15/06/P06005
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Experimentelle Teilchenphysik (ETP)
Institut für Prozessdatenverarbeitung und Elektronik (IPE)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 1748-0221
KITopen-ID: 1000122256
Erschienen in Journal of Instrumentation
Verlag IOP Publishing
Band 15
Heft 06
Seiten P06005
Vorab online veröffentlicht am 03.06.2020
Nachgewiesen in Scopus
Web of Science
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