Identification of hadronic tau lepton decays using a deep neural network - CMS Collaboration; Damarseçkin, Serdal (IOP Publishing Ltd, 2022)
A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (τh ) that originate from genuine tau leptons in the CMS detector against τh candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a τh 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 τh to pass the discriminator against jets increases by 10–30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient τh 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 τh reconstruction method are validated with LHC proton-proton collision data at √ = 13 TeV.
Keywords: calibration and fitting methods; cluster finding; Pattern recognition; Large detector systems for particle and astroparticle physics; Particle identification methods