The trigger system of the CMS detector is pivotal in the acquisition of data for physics measurements and searches. Studies of final states characterized by hadronic decays of tau leptons require the reconstruction and the identification of genuine tau leptons against quark- and gluon-initiated jets at the trigger level. This is a difficult task, particularly as improvements to the LHC have resulted in an increased number of interactions per bunch crossing in recent years. To address this challenge, a series of machine-learning algorithms with high identification efficiency and low computational cost have been incorporated into the high-level trigger for hadronically decaying tau leptons. In this paper, these developments and the trigger performance are summarized using data collected by the CMS experiment in proton-proton collisions at $ \sqrt{s} = $ 13.6 TeV in 2022--2023, corresponding to an integrated luminosity of 62 fb$ ^{-1} $.
A tau lepton identification algorithm, DEEPTAU, based on convolutional neural network techniques, has been developed in the CMS experiment to discriminate reconstructed hadronic decays of tau leptons ($ \tau_\mathrm{h} $) from quark or gluon jets and electrons and muons that are misreconstructed as $ \tau_\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 $ \tau_\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 $ \tau_\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 $ \tau_\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.
The DeepTau identification algorithm, based on deep neural network techniques, has been developed to reduce the fraction of jets, muons, and electrons misidentified as hadronically decaying tau leptons ($\tau_\mathrm{h}$) in the CMS experiment. The latest version of this algorithm includes domain adaptation by backpropagation, a technique that reduces data-to-simulation discrepancies in the region with the highest purity of genuine $\tau_\mathrm{h}$ candidates. Additionally, a refined training workflow improves classification performance, with a reduction of $30{-}50\%$ in the probability for jets to be misidentified as a $\tau_\mathrm{h}$ for a given reconstruction and identification efficiency. This note presents the main novelties introduced to the DeepTau algorithm and evaluates its performance in LHC proton-proton collision data at $\sqrt{s}=13$ and $13.6~\mathrm{TeV}$ collected in 2018 and 2022, respectively, with integrated luminosities of 60 and $35~\mathrm{fb}^{-1}$. The techniques to determine data-to-simulation scale factors are presented with a subset of results among the ones deployed centrally for CMS physics analyses.
MathiasTCK.bsky.social
CMS Publications
Hubu.de