engleski

Identification of hadronic tau lepton decays using a deep neural network

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.

High energy physics ; Experimental particle physics ; LHC ; CMS ; p p: scattering ; p p: colliding beams ; B: decay ; tau: hadronic decay ; interaction: gauge ; interaction: model ; transverse momentum: missing-energy ; new physics: search for ; mass spectrum: transverse ; black hole: quantum ; vector boson: mass ; W': leptonic decay ; sensitivity ; leptoquark: coupling ; CERN LHC Coll ; leptoquark: mass: lower limit ; anomaly ; channel cross section: upper limit ; effective field theory ; Higgs

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