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: | Karapınar, Güler CMS Collaboration |
Keywords: | Large detector-systems performance Pattern recognition, cluster finding, calibration and fitting methods |
Issue Date: | 2020 | Publisher: | IOP Publishing Ltd. | 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. | URI: | https://doi.org/10.1088/1748-0221/15/06/P06005 https://hdl.handle.net/10480 |
ISSN: | 1748-0221 |
Appears in Collections: | Rectorate / Rektörlük WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
Show full item record
CORE Recommender
SCOPUSTM
Citations
82
checked on Dec 2, 2023
WEB OF SCIENCETM
Citations
49
checked on Jun 17, 2023
Page view(s)
52
checked on Dec 4, 2023
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.