Identification of hadronic tau lepton decays using a deep neural network. (1st July 2022) - Tumasyan, A.; Adam, W.; Andrejkovic, J.W.; Bergauer, T.; Chatterjee, S.; Dragicevic, M.; Escalante Del Valle, A.; FrÃ¼hwirth, R.; Jeitler, M.; Krammer, N.; Lechner, L.; Liko, D.; et al.
Abstract: A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons ( Ï„ h ) that originate from genuine tau leptons in the CMS detector against Ï„ 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 Ï„ 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 Ï„ h to pass the discriminator against jets increases by 10â€"30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient Ï„ 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 Ï„ h reconstruction method are validated with LHC proton-proton collision data at âˆš s = 13 TeV.