Identification of tau leptons using a convolutional neural network with domain adaptation
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| Format: | Preprint |
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2025
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| _version_ | 1866914218265018368 |
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| author | CMS Collaboration |
| author_facet | CMS Collaboration |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05468 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Identification of tau leptons using a convolutional neural network with domain adaptation CMS Collaboration High Energy Physics - Experiment Instrumentation and Detectors 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. |
| title | Identification of tau leptons using a convolutional neural network with domain adaptation |
| topic | High Energy Physics - Experiment Instrumentation and Detectors |
| url | https://arxiv.org/abs/2511.05468 |