Hadronic tau lepton tagging with the CMS detector using domain adaptation to mitigate discrepancies between simulation and data
(2025)
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- Abstract
- The DeepTau identification algorithm, based on a Deep Neural Network model, has been developed to reduce the fraction of jets, muons and electrons misidentified as hadronically decaying tau leptons by the hadron-plus-strip algorithm in CMS.Its recently deployed version for the Run 3 of LHC, DeepTau v2.5, has brought several improvements to the existing algorithm, most importantly the inclusion of domain adaptation techniques specifically designed to reduce simulation-to-data discrepancies in the high-score region of the tagger. In this work, the main novelties of DeepTau v2.5 are briefly discussed and its improved performance are presented. The new model delivers a reduced jet fake rate by $ \thicksim$50$\%$ across the regions of interest and, thus, sets a new improved baseline for the tau identification task. Lastly, the calibration of the tagger is performed and proves that, as sought, the new version is less sensitive to simulation mismodelling.
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Citations
Mastrapasqua, P., & CMS. (2025). Hadronic tau lepton tagging with the CMS detector using domain adaptation to mitigate discrepancies between simulation and data. Proceedings of Science, ICHEP2024. https://doi.org/10.22323/1.476.1039 (Original work published 2025)