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: | WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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