Identification of hadronic tau lepton decays using a deep neural network
(2022) Journal of Instrumentation — Vol. 17 (2022)
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- Abstract
- A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons ($\tau_\mathrm{h}$) that originate from genuine tau leptons in the CMS detector against $\tau_\mathrm{h}$ candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a $\tau_\mathrm{h}$ candidate and employs a deep neural network with convolutional layers to efficiently process the inputs. This algorithm leads to a significantly improved performance compared with the previously used one. For example, the efficiency for a genuine $\tau_\mathrm{h}$ to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient $\tau_\mathrm{h}$ reconstruction is introduced that incorporates additional hadronic decay modes. The superior performance of the new algorithm to discriminate against jets, electrons, and muons and the improved $\tau_\mathrm{h}$ reconstruction method are validated with LHC proton-proton collision data at $\sqrt{s} =$ 13 TeV.
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Benecke, A., Bethani, A., Bruno, G., Bury, F., Caputo, C., David, P., Delaere, C., Donertas, I. S., Giammanco, A., Jaffel, K., Jain, S., Lemaitre, V., Mondal, K., Prisciandaro, J., Taliercio, A., Teklishyn, M., Tran, T. T., Vischia, P., Wertz, S., et al. (2022). Identification of hadronic tau lepton decays using a deep neural network. Journal of Instrumentation, 17. https://doi.org/10.1088/1748-0221/17/07/P07023 (Original work published 2022)