Identification of tau leptons using a convolutional neural network with domain adaptation


Hayrapetyan A., Makarenko V., Tumasyan A., Adam W., Andrejkovic J. W., Benato L., ...More

JOURNAL OF INSTRUMENTATION, vol.20, 2025 (SCI-Expanded, Scopus)

  • Publication Type: Article / Article
  • Volume: 20
  • Publication Date: 2025
  • Doi Number: 10.1088/1748-0221/20/12/p12032
  • Journal Name: JOURNAL OF INSTRUMENTATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, Index Islamicus, INSPEC
  • Istanbul University Affiliated: Yes

Abstract

A tau lepton identification algorithm, DEEPTAU, based on convolutional neural network techniques, has been developed in the CMS experiment to discriminate reconstructed hadronic decays of tau leptons (tau(h)) from quark or gluon jets and electrons and muons that are misreconstructed as tau(h) candidates. The latest version of this algorithm, v2.5, includes domain adaptation by backpropagation, a technique that reduces discrepancies between collision data and simulation in the region with the highest purity of genuine tau(h) candidates. Additionally, a refined training workflow improves classification performance with respect to the previous version of the algorithm, with a reduction of 30-50% in the probability for quark and gluon jets to be misidentified as tau(h) candidates for given reconstruction and identification efficiencies. This paper presents the novel improvements introduced in the DEEPTAU algorithm and evaluates its performance in LHC proton-proton collision data at root s = 13 and 13.6 TeV collected in 2018 and 2022 with integrated luminosities of 60 and 35 fb(-1), respectively. Techniques to calibrate the performance of the tau(h) identification algorithm in simulation with respect to its measured performance in real data are presented, together with a subset of results among those measured for use in CMS physics analyses.