Tumasyn, A., Adam, W., Andrejkovic, J. W., Bergauer, T., Chatterjee, S., Dragicevic, M., Del Valle, A. E., Frühwirth, R., Jeitler, M., Krammer, N., Lechner, L., Liko, D., Mikulec, I., Paulitsch, P., Pitters, F. M., Schieck, J., Schöfbeck, R., Schwarz, D., & Vetens, W. (2022). Identification of hadronic tau lepton decays using a deep neural network. Journal of Instrumentation, 17, Article P07023. https://doi.org/10.1088/1748-0221/17/07/P07023
E141-03 - Forschungsbereich Nuclear and Particle Physics
Journal of Instrumentation
IOP PUBLISHING LTD
A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (τhτh) that originate from genuine tau leptons in the CMS detector against τhτh candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a τhτh 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 τhτh to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient τhτh 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 τhτh reconstruction method are validated with LHC proton-proton collision data at s=s
= 13 TeV.
Note:Replaced with the published version. Added the journal reference and the DOI. All the figures and tables can be found at http://cms-results.web.cern.ch/cms-results/public-results/publications/TAU-20-001 (CMS Public Pages)