Publication: Identification of hadronic tau lepton decays using a deep neural network
Дата
2022
Авторы
Chadeeva, M.
Oskin, A.
Parygin, P.
Popova, E.
Selivanova, D.
Matveev, V.
Zhemchugov, E.
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A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (τ h) that originate from genuine tau leptons in the CMS detector against τ h candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a τ h candidate and employs a deep neural network with convolutional layers to efficiently process the inputs. This algorithm leads to a significantly improved performance compared with the previously used one. For example, the efficiency for a genuine τ h to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient τ h reconstruction is introduced that incorporates additional hadronic decay modes. The superior performance of the new algorithm to discriminate against jets, electrons, and muons and the improved τ h reconstruction method are validated with LHC proton-proton collision data at s = 13 TeV. © 2022 CERN.
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Identification of hadronic tau lepton decays using a deep neural network / Tumasyan, A. [et al.] // Journal of Instrumentation. - 2022. - 17. - № 7. - 10.1088/1748-0221/17/07/P07023