Identification of tau leptons using a convolutional neural network with domain adaptation
(2025) Journal of Instrumentation — Vol. 20 (2025)
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- 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 ($τ_\mathrm{h}$) from quark or gluon jets and electrons and muons that are misreconstructed as $τ_\mathrm{h}$ 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 $τ_\mathrm{h}$ 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 $τ_\mathrm{h}$ 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 $τ_\mathrm{h}$ 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.
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Bein, S., Benecke, A., Bethani, A., Bruno, G., Cappati, A., de Favereau de Jeneret, J., Delaere, C., Giammanco, A., Guzel, A. O., Lemaitre, V., Lidrych, J., Malek, P., Mastrapasqua, P., Turkcapar, S., CMS, & et al. (2025). Identification of tau leptons using a convolutional neural network with domain adaptation. Journal of Instrumentation, 20. https://doi.org/10.1088/1748-0221/20/12/P12032 (Original work published 2025)