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

CMS Collaboration, Freya Blekman, Emil Sørensen Bols, Simranjit Singh Chhibra, Jorgen D'Hondt, Jarne De Clercq, Denys Lontkovskyi, Steven Lowette, Ivan Marchesini, Seth Moortgat, Quentin Python, Kirill Skovpen, Stefaan Tavernier, Walter Van Doninck, Petra Van Mulders, Douglas Burns, Douglas John Paul Burns

Research output: Contribution to journalArticlepeer-review

79 Citations (Scopus)
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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 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.

Original languageEnglish
Article numberP06005
Number of pages83
Issue number6
Publication statusPublished - 3 Jun 2020

Bibliographical note

Replaced with the published version. Added the journal reference and the DOI. All the figures and tables can be found at (CMS Public Pages)


  • hep-ex
  • physics.ins-det


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