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The Tau Neural Classifier algorithm reconstructs the decay mode of the |
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tau--candidate and uses this information to select the discriminant used to |
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determine whether the tau--candidate should be classified as signal or |
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background. To optimize the discrimination for each of the different decay |
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modes, the TaNC uses an ensemble of neural nets. Each neural net corresponds to |
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one of the dominant hadronic decay modes of the tau lepton. These selected |
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hadronic decays constitue 95\% of all hadronic tau decays. Tau--candidates with |
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other decay modes are immediately tagged as background. \fixme(when to talk |
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about lead track req?) |
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tau--candidate and then feeds the tau--candidate to a discriminator associated |
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to the decay mode to make the classification decision. Each discriminator |
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therefore maps to a reconstructed decay mode in a one-to-one fashion. To |
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optimize the discrimination for each of the different decay modes, the TaNC uses |
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an ensemble of neural nets. Each neural net corresponds to one of the dominant |
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hadronic decay modes of the tau lepton. These selected hadronic decays |
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constitute 95\% of all hadronic tau decays. Tau--candidates with reconstructed |
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decay modes not in the set of dominant hadronic modes are immediately tagged as |
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background. |
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