Identification of hadronic tau lepton decays using a deep neural network

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 part...

Descripción completa

Detalles Bibliográficos
Autores: Tumasyan, A., Brochero Cifuentes, Javier Andrés|||0000-0003-2093-7856, Cabrillo Bartolomé, José Iban|||0000-0002-0367-4022, Calderón Tazón, Alicia|||0000-0002-7205-2040, Duarte Campderros, Jorge|||0000-0003-0687-5214, Fernández García, Marcos|||0000-0002-4824-1087, Fernández Madrazo, Celia|||0000-0001-9748-4336, Fernández Manteca, Pedro José|||0000-0003-2566-7496, García Alonso, Andrea, Gómez Gramuglio, Gervasio|||0000-0002-1077-6553, Martínez Rivero, Celso, Martínez Ruiz del Árbol, Pablo|||0000-0002-7737-5121, Matorras Weinig, Francisco|||0000-0003-4295-5668, Matorras Cuevas, Pablo|||0000-0001-7481-7273, Piedra Gómez, Jonatan|||0000-0002-9157-1700, Prieëls, Cedric, Rodrigo Anoro, Teresa, Ruiz Jimeno, Alberto|||0000-0002-3639-0368, Scodellaro, Luca|||0000-0002-4974-8330, Vila Álvarez, Iván |||0000-0002-6797-7209, Vizán García, Jesús Manuel|||0000-0002-6823-8854
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad de Cantabria (UC)
Repositorio:UCrea Repositorio Abierto de la Universidad de Cantabria
Idioma:inglés
OAI Identifier:oai:repositorio.unican.es:10902/28644
Acceso en línea:https://hdl.handle.net/10902/28644
Access Level:acceso abierto
Palabra clave:Large detector systems for particle and astroparticle physics
Particle identification methods
Pattern recognition
Cluster finding
Calibration and fitting methods
id ES_9cc8f07bb8a506c65b9310be9dd6ccbb
oai_identifier_str oai:repositorio.unican.es:10902/28644
network_acronym_str ES
network_name_str España
repository_id_str
spelling Identification of hadronic tau lepton decays using a deep neural networkTumasyan, A.Brochero Cifuentes, Javier Andrés|||0000-0003-2093-7856 Cabrillo Bartolomé, José Iban|||0000-0002-0367-4022Calderón Tazón, Alicia|||0000-0002-7205-2040Duarte Campderros, Jorge|||0000-0003-0687-5214Fernández García, Marcos|||0000-0002-4824-1087Fernández Madrazo, Celia|||0000-0001-9748-4336Fernández Manteca, Pedro José|||0000-0003-2566-7496García Alonso, AndreaGómez Gramuglio, Gervasio|||0000-0002-1077-6553Martínez Rivero, CelsoMartínez Ruiz del Árbol, Pablo|||0000-0002-7737-5121Matorras Weinig, Francisco|||0000-0003-4295-5668Matorras Cuevas, Pablo|||0000-0001-7481-7273Piedra Gómez, Jonatan|||0000-0002-9157-1700Prieëls, CedricRodrigo Anoro, TeresaRuiz Jimeno, Alberto|||0000-0002-3639-0368Scodellaro, Luca|||0000-0002-4974-8330Vila Álvarez, Iván |||0000-0002-6797-7209Vizán García, Jesús Manuel|||0000-0002-6823-8854Large detector systems for particle and astroparticle physicsParticle identification methodsPattern recognitionCluster findingCalibration and fitting methodsA 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 √ �������� = 13 TeVUniversidad de Cantabria20222022-01-01journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articlehttps://hdl.handle.net/10902/28644Journal of Instrumentation, 2022, 17, P07023reponame:UCrea Repositorio Abierto de la Universidad de Cantabriainstname:Universidad de Cantabria (UC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositorio.unican.es:10902/286442026-06-02T12:39:31Z
dc.title.none.fl_str_mv Identification of hadronic tau lepton decays using a deep neural network
title Identification of hadronic tau lepton decays using a deep neural network
spellingShingle Identification of hadronic tau lepton decays using a deep neural network
Tumasyan, A.
Large detector systems for particle and astroparticle physics
Particle identification methods
Pattern recognition
Cluster finding
Calibration and fitting methods
title_short Identification of hadronic tau lepton decays using a deep neural network
title_full Identification of hadronic tau lepton decays using a deep neural network
title_fullStr Identification of hadronic tau lepton decays using a deep neural network
title_full_unstemmed Identification of hadronic tau lepton decays using a deep neural network
title_sort Identification of hadronic tau lepton decays using a deep neural network
dc.creator.none.fl_str_mv Tumasyan, A.
Brochero Cifuentes, Javier Andrés|||0000-0003-2093-7856
Cabrillo Bartolomé, José Iban|||0000-0002-0367-4022
Calderón Tazón, Alicia|||0000-0002-7205-2040
Duarte Campderros, Jorge|||0000-0003-0687-5214
Fernández García, Marcos|||0000-0002-4824-1087
Fernández Madrazo, Celia|||0000-0001-9748-4336
Fernández Manteca, Pedro José|||0000-0003-2566-7496
García Alonso, Andrea
Gómez Gramuglio, Gervasio|||0000-0002-1077-6553
Martínez Rivero, Celso
Martínez Ruiz del Árbol, Pablo|||0000-0002-7737-5121
Matorras Weinig, Francisco|||0000-0003-4295-5668
Matorras Cuevas, Pablo|||0000-0001-7481-7273
Piedra Gómez, Jonatan|||0000-0002-9157-1700
Prieëls, Cedric
Rodrigo Anoro, Teresa
Ruiz Jimeno, Alberto|||0000-0002-3639-0368
Scodellaro, Luca|||0000-0002-4974-8330
Vila Álvarez, Iván |||0000-0002-6797-7209
Vizán García, Jesús Manuel|||0000-0002-6823-8854
author Tumasyan, A.
author_facet Tumasyan, A.
Brochero Cifuentes, Javier Andrés|||0000-0003-2093-7856
Cabrillo Bartolomé, José Iban|||0000-0002-0367-4022
Calderón Tazón, Alicia|||0000-0002-7205-2040
Duarte Campderros, Jorge|||0000-0003-0687-5214
Fernández García, Marcos|||0000-0002-4824-1087
Fernández Madrazo, Celia|||0000-0001-9748-4336
Fernández Manteca, Pedro José|||0000-0003-2566-7496
García Alonso, Andrea
Gómez Gramuglio, Gervasio|||0000-0002-1077-6553
Martínez Rivero, Celso
Martínez Ruiz del Árbol, Pablo|||0000-0002-7737-5121
Matorras Weinig, Francisco|||0000-0003-4295-5668
Matorras Cuevas, Pablo|||0000-0001-7481-7273
Piedra Gómez, Jonatan|||0000-0002-9157-1700
Prieëls, Cedric
Rodrigo Anoro, Teresa
Ruiz Jimeno, Alberto|||0000-0002-3639-0368
Scodellaro, Luca|||0000-0002-4974-8330
Vila Álvarez, Iván |||0000-0002-6797-7209
Vizán García, Jesús Manuel|||0000-0002-6823-8854
author_role author
author2 Brochero Cifuentes, Javier Andrés|||0000-0003-2093-7856
Cabrillo Bartolomé, José Iban|||0000-0002-0367-4022
Calderón Tazón, Alicia|||0000-0002-7205-2040
Duarte Campderros, Jorge|||0000-0003-0687-5214
Fernández García, Marcos|||0000-0002-4824-1087
Fernández Madrazo, Celia|||0000-0001-9748-4336
Fernández Manteca, Pedro José|||0000-0003-2566-7496
García Alonso, Andrea
Gómez Gramuglio, Gervasio|||0000-0002-1077-6553
Martínez Rivero, Celso
Martínez Ruiz del Árbol, Pablo|||0000-0002-7737-5121
Matorras Weinig, Francisco|||0000-0003-4295-5668
Matorras Cuevas, Pablo|||0000-0001-7481-7273
Piedra Gómez, Jonatan|||0000-0002-9157-1700
Prieëls, Cedric
Rodrigo Anoro, Teresa
Ruiz Jimeno, Alberto|||0000-0002-3639-0368
Scodellaro, Luca|||0000-0002-4974-8330
Vila Álvarez, Iván |||0000-0002-6797-7209
Vizán García, Jesús Manuel|||0000-0002-6823-8854
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidad de Cantabria
dc.subject.none.fl_str_mv Large detector systems for particle and astroparticle physics
Particle identification methods
Pattern recognition
Cluster finding
Calibration and fitting methods
topic Large detector systems for particle and astroparticle physics
Particle identification methods
Pattern recognition
Cluster finding
Calibration and fitting methods
description 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 √ �������� = 13 TeV
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10902/28644
url https://hdl.handle.net/10902/28644
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Journal of Instrumentation, 2022, 17, P07023
reponame:UCrea Repositorio Abierto de la Universidad de Cantabria
instname:Universidad de Cantabria (UC)
instname_str Universidad de Cantabria (UC)
reponame_str UCrea Repositorio Abierto de la Universidad de Cantabria
collection UCrea Repositorio Abierto de la Universidad de Cantabria
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869414688257736704
score 15,301603