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The Tau Neural Classifier algorithm reconstructs the decay mode of the
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tau--candidate and then feeds the tau--candidate to a discriminator associated
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to that 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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