@article{10902/28644,
year = {2022},
url = {https://hdl.handle.net/10902/28644},
abstract = {A new algorithm is presented to discriminate reconstructed hadronic decays of tau
leptons (τh
) that originate from genuine tau leptons in the CMS detector against τh
candidates that
originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from
all reconstructed particles in the vicinity of a τh
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 τh
to pass the discriminator against jets increases by 10–30% for a given efficiency for
quark and gluon jets. Furthermore, a more efficient τh
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 τh
reconstruction method are validated with
LHC proton-proton collision data at √
𝑠�������� = 13 TeV},
publisher = {Journal of Instrumentation, 2022, 17, P07023},
title = {Identification of hadronic tau lepton decays using a deep neural network},
author = {Tumasyan, A. and Brochero Cifuentes, Javier Andrés and Cabrillo Bartolomé, José Iban and Calderón Tazón, Alicia and Duarte Campderros, Jorge and Fernández García, Marcos and Fernández Madrazo, Celia and Fernández Manteca, Pedro José and García Alonso, Andrea and Gómez Gramuglio, Gervasio and Martínez Rivero, Celso and Martínez Ruiz del Árbol, Pablo and Matorras Weinig, Francisco and Matorras Cuevas, Pablo and Piedra Gómez, Jonatan and Prieëls, Cedric and Rodrigo Anoro, Teresa and Ruiz Jimeno, Alberto and Scodellaro, Luca and Vila Álvarez, Iván  and Vizán García, Jesús Manuel},
}