Identification of hadronic tau lepton decays using a deep neural network


Tumasyan A., Adam W., Andrejkovic J., Bergauer T., Chatterjee S., Dragicevic M., ...More

Journal of Instrumentation, vol.17, no.7, 2022 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 17 Issue: 7
  • Publication Date: 2022
  • Doi Number: 10.1088/1748-0221/17/07/p07023
  • Journal Name: Journal of Instrumentation
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, Index Islamicus, INSPEC
  • Keywords: calibration and fitting methods, cluster finding, Large detector systems for particle and astroparticle physics, Particle identification methods, Pattern recognition
  • Hacettepe University Affiliated: Yes

Abstract

© 2022 CERN.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.