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Title
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Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
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Author
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Institution/Organisation
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CMS Collaboration
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Abstract
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| 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. |
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Language
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English
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Source (journal)
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Journal of instrumentation. - Bristol, 2006, currens
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Publication
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Bristol
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Institute of Physics
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2020
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ISSN
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1748-0221
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DOI
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10.1088/1748-0221/15/06/P06005
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Volume/pages
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15
:6
(2020)
, 87 p.
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Article Reference
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P06005
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ISI
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000545350900005
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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Full text (open access)
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