Identification of hadronic tau lepton decays using a deep neural network
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Date
2022
Publication Type
Journal Article
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yes
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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.
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published
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Volume
17 (7)
Pages / Article No.
Publisher
IOP Publishing
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Subject
Large detector systems for particle and astroparticle physics; Particle identification methods; Pattern recognition; cluster finding; calibration and fitting methods
Organisational unit
03593 - Dissertori, Günther / Dissertori, Günther
03904 - Wallny, Rainer / Wallny, Rainer
09720 - de Cosa, Annapaola / de Cosa, Annapaola