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

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http://hdl.handle.net/10138/321572

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The CMS Collaboration , Eerola , P , Forthomme , L , Kirschenmann , H , Osterberg , K , Voutilainen , M , Garcia , F , Havukainen , J , Heikkila , J K , Kim , M , Kinnunen , R , Lampen , T , Lassila-Perini , K , Laurila , S , Lehti , S , Linden , T , Luukka , P , Mäenpää , T , Siikonen , H , Tuominen , E , Tuominiemi , J , Viinikainen , J , Karimäki , V , Tuuva , T & Sirunyan , A M 2020 , ' Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques ' , Journal of Instrumentation , vol. 15 , no. 6 , 06005 . https://doi.org/10.1088/1748-0221/15/06/P06005

Title: Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
Author: The CMS collaboration; Eerola, P.; Forthomme, L.; Kirschenmann, H.; Osterberg, K.; Voutilainen, M.; Garcia, F.; Havukainen, J.; Heikkila, J. K.; Kim, M.; Kinnunen, R.; Lampen, T.; Lassila-Perini, K.; Laurila, S.; Lehti, S.; Linden, T.; Luukka, P.; Mäenpää, T.; Siikonen, H.; Tuominen, E.; Tuominiemi, J.; Viinikainen, J.; Karimäki, V.; Tuuva, T.; Sirunyan, A. M.
Contributor: University of Helsinki, Department of Physics
University of Helsinki, Department of Physics
University of Helsinki, Department of Physics
University of Helsinki, Department of Physics
University of Helsinki, Department of Physics
University of Helsinki, Helsinki Institute of Physics
University of Helsinki, Helsinki Institute of Physics
University of Helsinki, Helsinki Institute of Physics
University of Helsinki, Helsinki Institute of Physics
University of Helsinki, Helsinki Institute of Physics
University of Helsinki, Helsinki Institute of Physics
University of Helsinki, Helsinki Institute of Physics
University of Helsinki, Helsinki Institute of Physics
University of Helsinki, Helsinki Institute of Physics
University of Helsinki, Helsinki Institute of Physics
University of Helsinki, Helsinki Institute of Physics
University of Helsinki, Helsinki Institute of Physics
University of Helsinki, Helsinki Institute of Physics
University of Helsinki, Helsinki Institute of Physics
University of Helsinki, Helsinki Institute of Physics
University of Helsinki, University of Illinois, Chicago
University of Helsinki, Helsinki Institute of Physics
Date: 2020-06
Language: eng
Number of pages: 87
Belongs to series: Journal of Instrumentation
ISSN: 1748-0221
URI: http://hdl.handle.net/10138/321572
Abstract: 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 root S = 13 TeV, 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.
Subject: 114 Physical sciences
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