| 1 |
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%Due to the low signal rates for many new physics scenarios and large QCD |
| 2 |
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%backgrounds, efficiently identifying tau leptons while maintaining an extremely |
| 3 |
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%low mis-tag rate will be an important part of the CMS physics program. The CMS |
| 4 |
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%particle flow algorithm combines all sub-detectors to provide a global |
| 5 |
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%reconstruction of a collision event and potentially improves spatial and energy |
| 6 |
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%resolution. A new algorithm for identifying hadronic tau decays, the Tau Neural |
| 7 |
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%Classifier (TaNC) is presented in this paper. Using the reconstructed objects |
| 8 |
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%from particle flow, the algorithm reconstructs the hadronic decay of the tau |
| 9 |
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%lepton and uses an ensemble of neural nets to discriminate against common |
| 10 |
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%background. This strategy provides a large performance improvement with respect |
| 11 |
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%to previous CMS tau identification strategies and can potentially increase the |
| 12 |
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%reach of many CMS searches for physics beyond the Standard Model. A technical |
| 13 |
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%description of the algorithm and measurements of performance are included. |
| 14 |
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|
| 1 |
|
The Tau Neural Classifier (TaNC) is a novel algorithm for identification of |
| 2 |
|
hadronic tau decays. The algorithm includes two components, the reconstruction |
| 3 |
|
of tau lepton hadronic decay modes and discrimination of tau lepton hadronic |
