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Identification of hadronic tau lepton decays using a deep neural network

Abstract: A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (?h ) that originate from genuine tau leptons in the CMS detector against ?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 ?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 ?h to pass the discriminator against jets increases by 10?30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient ?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 ?h reconstruction method are validated with LHC proton-proton collision data at ? ?? = 13 TeV.

Otras publicaciones de la misma revista o congreso con autores/as de la Universidad de Cantabria

 Autoría: Tumasyan A., Adam W., Andrejkovic J.W., Bergauer T., Chatterjee S., Dragicevic M., Escalante Del Valle A., Frühwirth R., Jeitler M., Krammer N., Lechner L., Liko D., Mikulec I., Paulitsch P., Pitters F.M., Schieck J., Schöfbeck R., Schwarz D., Templ S., Waltenberger W., Wulz C.E., Chekhovsky V., Litomin A., Makarenko V., Darwish M.R., De Wolf E.A., Janssen T., Kello T., Lelek A., Rejeb Sfar H., Van Mechelen P., Van Putte S., Van Rem

 Fuente: Journal of Instrumentation, 2022, 17, P07023

 Año de publicación: 2022

Nº de páginas: 53

Tipo de publicación: Artículo de Revista

 DOI: 10.1088/1748-0221/17/07/P07023

ISSN: 1748-0221

Url de la publicación: https://doi.org/10.1088/1748-0221/17/07/P07023