KIT | KIT-Bibliothek | Impressum | Datenschutz

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.; ... 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.

Verlagsausgabe §
DOI: 10.5445/IR/1000122256
Veröffentlicht am 29.07.2020
DOI: 10.1088/1748-0221/15/06/P06005
Zitationen: 83
Web of Science
Zitationen: 68
Zitationen: 64
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 Institute of Physics Publishing Ltd (IOP Publishing Ltd)
Band 15
Heft 06
Seiten P06005
Vorab online veröffentlicht am 03.06.2020
Nachgewiesen in Dimensions
Web of Science
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page