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@ARTICLE{Hayrapetyan:643099,
      author       = {Hayrapetyan, Aram and others},
      collaboration = {{CMS Collaboration}},
      title        = {{I}dentification of tau leptons using a convolutional
                      neural network with domain adaptation},
      journal      = {Journal of Instrumentation},
      volume       = {20},
      number       = {12},
      issn         = {1748-0221},
      address      = {London},
      publisher    = {Inst. of Physics},
      reportid     = {PUBDB-2025-05843, arXiv:2511.05468. CMS-TAU-24-001.
                      CERN-EP-2025-233},
      pages        = {P12032},
      year         = {2025},
      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 ($τ_\mathrm{h}$) 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.},
      cin          = {CMS},
      ddc          = {610},
      cid          = {I:(DE-H253)CMS-20120731},
      pnm          = {611 - Fundamental Particles and Forces (POF4-611) /
                      HIDSS-0002 - DASHH: Data Science in Hamburg - Helmholtz
                      Graduate School for the Structure of Matter
                      $(2019_IVF-HIDSS-0002)$ / DFG project G:(GEPRIS)390833306 -
                      EXC 2121: Das Quantisierte Universum II (390833306)},
      pid          = {G:(DE-HGF)POF4-611 / $G:(DE-HGF)2019_IVF-HIDSS-0002$ /
                      G:(GEPRIS)390833306},
      experiment   = {EXP:(DE-H253)LHC-Exp-CMS-20150101},
      typ          = {PUB:(DE-HGF)16},
      eprint       = {2511.05468},
      howpublished = {arXiv:2511.05468},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2511.05468;\%\%$},
      doi          = {10.1088/1748-0221/20/12/P12032},
      url          = {https://bib-pubdb1.desy.de/record/643099},
}