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.

Tumasyan, A., Adam, W., Andrejkovic, J., Bergauer, T., Chatterjee, S., Dragicevic, M., et al. (2022). Identification of hadronic tau lepton decays using a deep neural network. JOURNAL OF INSTRUMENTATION, 17(7) [10.1088/1748-0221/17/07/P07023].

Identification of hadronic tau lepton decays using a deep neural network

Benaglia A.;Boldrini G.;Brivio F.;Cetorelli F.;De Guio F.;Dinardo M. E.;Ghezzi A.;Govoni P.;Guzzi L.;Lucchini M. T.;Malberti M.;Massironi A.;Moroni L.;Paganoni M.;Pinolini B. S.;Ragazzi S.;Tabarelli de Fatis T.;Valsecchi D.;Zuolo D.;Ortona G.;Gerosa R.;
2022

Abstract

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.
Articolo in rivista - Articolo scientifico
calibration and fitting methods; cluster finding; Large detector systems for particle and astroparticle physics; Particle identification methods; Pattern recognition;
English
2022
17
7
P07023
none
Tumasyan, A., Adam, W., Andrejkovic, J., Bergauer, T., Chatterjee, S., Dragicevic, M., et al. (2022). Identification of hadronic tau lepton decays using a deep neural network. JOURNAL OF INSTRUMENTATION, 17(7) [10.1088/1748-0221/17/07/P07023].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/429401
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