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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