Identification of hadronic tau lepton decays using a deep neural network

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