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friis |
1.1 |
The TaNC algorithm has been optimized for the early stages of LHC operation.
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The quality selections applied by the decay mode reconstruction algorithm will
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need to be re--tuned and the neural networks retrained after each significant
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change in instantaneous luminosity to remove the effects of multiple
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interactions in the same bunch crossing, and after collision energy changes due
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to the increased energy of the multiple scattering events.
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The dependence of the algorithm on the robustness of Monte
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Carlo simulations can be minimized by using real events as the background
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training sample. As the QCD di--jet production rate is many orders of magnitude
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larger than that of tau leptons, it is relatively straightforward to obtain a
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pure background sample to use in training with early CMS datasets.
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