Identification of hadronic tau lepton decays using a deep neural network

The CMS Collaboration

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (τ h) that originate from genuine tau leptons in the CMS detector against τ h candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a τ h candidate and employs a deep neural network with convolutional layers to efficiently process the inputs. This algorithm leads to a significantly improved performance compared with the previously used one. For example, the efficiency for a genuine τ h to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient τ h reconstruction is introduced that incorporates additional hadronic decay modes. The superior performance of the new algorithm to discriminate against jets, electrons, and muons and the improved τ h reconstruction method are validated with LHC proton-proton collision data at s = 13 TeV.

Original languageEnglish
Article numberP07023
JournalJournal of Instrumentation
Volume17
Issue number7
DOIs
Publication statusPublished - 2022 Jul 1

Keywords

  • Large detector systems for particle and astroparticle physics
  • Particle identification methods
  • Pattern recognition
  • calibration and fitting methods
  • cluster finding

ASJC Scopus subject areas

  • Instrumentation
  • Mathematical Physics

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