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%Due to the low signal rates for many new physics scenarios and large QCD
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%backgrounds, efficiently identifying tau leptons while maintaining an extremely
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%low mis-tag rate will be an important part of the CMS physics program. The CMS
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%particle flow algorithm combines all sub-detectors to provide a global
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%reconstruction of a collision event and potentially improves spatial and energy
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%resolution. A new algorithm for identifying hadronic tau decays, the Tau Neural
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%Classifier (TaNC) is presented in this paper. Using the reconstructed objects
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%from particle flow, the algorithm reconstructs the hadronic decay of the tau
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%lepton and uses an ensemble of neural nets to discriminate against common
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%background. This strategy provides a large performance improvement with respect
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%to previous CMS tau identification strategies and can potentially increase the
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%reach of many CMS searches for physics beyond the Standard Model. A technical
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%description of the algorithm and measurements of performance are included.
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The Tau Neural Classifier (TaNC) is a novel algorithm for identification of
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hadronic tau decays. The algorithm includes two components, the reconstruction
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of tau lepton hadronic decay modes and discrimination of tau lepton hadronic
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decays from quark and gluon jets. The reconstruction of decay modes is based
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on the reconstruction of individual charged hadrons and photons by the
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particle--flow algorithm and is utilized in the discrimination to train a set
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of neural networks using input variables that are sensitive to particular decay
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modes. We observe a significant improvement in identification performance in
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comparison to previous algorithms.
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