Identification of hadronic tau lepton decays using a deep neural network

Abstract

A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (${\tau }_{\mathrm{h}}$) that originate from genuine tau leptons in the CMS detector against ${\tau }_{\mathrm{h}}$ candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a ${\tau }_{\mathrm{h}}$ 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 ${\tau }_{\mathrm{h}}$ to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient ${\tau }_{\mathrm{h}}$ 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 ${\tau }_{\mathrm{h}}$ reconstruction method are validated with LHC proton-proton collision data at $\sqrt{s}=$ 13 TeV.

Replaced with the published version. Added the journal reference and the DOI. All the figures and tables can be found at http://cms-results.web.cern.ch/cms-results/public-results/publications/TAU-20-001 (CMS Public Pages)

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