Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/10480
Title: Identification of Heavy, Energetic, Hadronically Decaying Particles Using Machine-Learning Techniques
Authors: Sirunyan, A.M.
Tumasyan, A.
Adam, W.
Ambrogi, F.
Bergauer, T.
Dragicevic, M.
Okhotnikov, V.
Keywords: Large Detector-Systems Performance
Pattern Recognition, Cluster Finding, Calibration And Fitting Methods
Publisher: Institute of Physics
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 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. © 2020 CERN for the benefit of the CMS collaboration..
URI: https://doi.org/10.1088/1748-0221/15/06/P06005
ISSN: 1748-0221
Appears in Collections:Rectorate / Rektörlük
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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