Identification of tau leptons using a convolutional neural network with domain adaptation
Journal Article
Overview
abstract
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; (τ; h; ) from quark or gluon jets and; electrons and muons that are misreconstructed as; τ; 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; τ; 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 τ; 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 √(; 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 τ; 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.;
