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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. Using the reconstructed objects from particle flow, an algorithm |
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using an ensemble of neural nets corresponding to different tau decay |
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resonances has been developed. This algorithm provides a large performance |
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improvement with respect to previous CMS tau identification strategies and can |
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potentially increase the reach of many CMS searches for physics beyond the |
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Standard Model. Preliminary results are presented in this summary. |
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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. |
