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Title: Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques

Journal Article · · Journal of Instrumentation
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  1. University of Colorado Boulder
+ Show Author Affiliations

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 $$\sqrt{s}$$ = 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.

Research Organization:
Univ. of Colorado, Boulder, CO (United States)
Sponsoring Organization:
USDOE
Contributing Organization:
The CMS collaboration
Grant/Contract Number:
SC0010005
OSTI ID:
1637518
Alternate ID(s):
OSTI ID: 1637642; OSTI ID: 1777207; OSTI ID: 1888090; OSTI ID: 1889910; OSTI ID: 23048693
Journal Information:
Journal of Instrumentation, Journal Name: Journal of Instrumentation Journal Issue: 06 Vol. 15; ISSN 1748-0221
Publisher:
Institute of Physics (IOP)Copyright Statement
Country of Publication:
United States
Language:
English

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