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

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 (${\tau }_{\mathrm{h}}$) from quark or gluon jets and electrons and muons that are misreconstructed as ${\tau }_{\mathrm{h}}$ 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 ${\tau }_{\mathrm{h}}$ 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 ${\tau }_{\mathrm{h}}$ 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 $\sqrt{s}$ = 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 ${\tau }_{\mathrm{h}}$ 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.

Replaced with the published version. Added the journal reference and the DOI. All the figures and tables can be found at http://cms-results.web.cern.ch/cms-results/public-results/publications/TAU-24-001 (CMS Public Pages)

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Cited by in corpus (1)

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