Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
Name
Sirunyan_2020_J._Inst._15_P06005.pdf
Description
Published version
Size
5.35 MB
Format
Adobe PDF
Checksum (MD5)
d988699fb27d4c2722641cc50186c281
Author(s)
The CMS Collaboration
Date Issued
2020
Journal
Journal of Instrumentation
Publisher
IOP Publishing
Version
Final published version
Abstract
© 2020 CERN for the benefit of the CMS collaboration.. 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.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Massachusetts Institute of Technology. Laboratory for Nuclear Science
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1088/1748-0221/15/06/P06005
