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.

Identification of hadronic tau lepton decays using a deep neural network

Bertacchi, V.;Ligabue, F.
Membro del Collaboration Group
;
Manca, E.;Mandorli, G.;Ramirez Sanchez, G.;Roy Chowdhury, S.;
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. © 2022 CERN.
2022
Settore FIS/01 - Fisica Sperimentale
Large detector systems for particle and astroparticle physics; Particle identification methods; Pattern recognition, cluster finding, calibration and fitting methods
   Advanced Multi-Variate Analysis for New Physics Searches at the LHC
   AMVA4NewPhysics
   European Commission
   Horizon 2020 Framework Programme
   675440

   Search for Higgs bosons decaying to charm quarks
   HIGCC
   European Commission
   Horizon 2020 Framework Programme
   724704

   Direct and indirect searches for new physics in events with top quarks using LHC proton-proton collisions at the CMS detector
   LHCTOPVLQ
   European Commission
   Horizon 2020 Framework Programme
   752730

   Majorana neutrino discovery strategy with CMS
   MajorNet
   European Commission
   Horizon 2020 Framework Programme
   758316

   International Training Network for Statistics in High Energy Physics and Society
   INSIGHTS
   European Commission
   Horizon 2020 Framework Programme
   765710

   The strong interaction at the frontier of knowledge: fundamental research and applications
   STRONG-2020
   European Commission
   Horizon 2020 Framework Programme
   824093

   International, Interdisciplinary & Intersectoral Postdoctoral Fellowships at the Paul Scherrer Institut
   PSI-FELLOW-III-3i
   European Commission
   Horizon 2020 Framework Programme
   884104
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/139591
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