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Title: | Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques |
Authors: | CMS Collaboration Sirunyan, A. M. DUBE, SOURABH KANSAL, B. KAPOOR, A. KOTHEKAR, K. PANDEY, S. RANE, A. RASTOGI, A. SHARMA, SEEMA et al. Dept. of Physics |
Keywords: | CMS Physics TOC-JUL-2020 2020 2020-JUL-WEEK4 |
Issue Date: | Jun-2020 |
Publisher: | IOP Publishing |
Citation: | Journal of Instrumentation, 15(6). |
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. |
URI: | http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/4896 https://doi.org/10.1088/1748-0221/15/06/P06005 |
ISSN: | 1748-0221 |
Appears in Collections: | JOURNAL ARTICLES |
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