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

The CMS collaboration

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Article numberP06005
Number of pages87
JournalJournal of Instrumentation
Volume15
Issue number6
DOIs
Publication statusPublished - 1 Jun 2020

Keywords

  • Large detector-systems performance
  • Pattern recognition, cluster finding, calibration and fitting methods
  • ANNIHILATION
  • ALGORITHMS

Fingerprint Dive into the research topics of 'Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques'. Together they form a unique fingerprint.

Cite this