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dc.contributor.authorGarcía Gámez, Diego 
dc.contributor.authorZamorano García, Bruno 
dc.contributor.authorDune Collaboration
dc.date.accessioned2021-03-15T08:59:49Z
dc.date.available2021-03-15T08:59:49Z
dc.date.issued2020
dc.identifier.citationAbi, B., Acciarri, R., Acero, M. A., Adamov, G., Adams, D., Adinolfi, M., ... & Chiriacescu, A. (2020). Neutrino interaction classification with a convolutional neural network in the DUNE far detector. Physical Review D, 102(9), 092003.es_ES
dc.identifier.urihttp://hdl.handle.net/10481/67204
dc.description.abstractThe Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure CP-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2–5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino energies above 2 GeV. When considering all electron neutrino and antineutrino interactions as signal, a selection purity of 90% is achieved. These event selections are critical to maximize the sensitivity of the experiment to CP-violating effects.es_ES
dc.description.sponsorshipFermi Research Alliance, LLC (FRA) DE-AC02-07CH11359es_ES
dc.description.sponsorshipNational Council for Scientific and Technological Development (CNPq)es_ES
dc.description.sponsorshipCarlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro (FAPERJ)es_ES
dc.description.sponsorshipFAPEG, Braziles_ES
dc.description.sponsorshipFundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)es_ES
dc.description.sponsorshipCanada Foundation for Innovationes_ES
dc.description.sponsorshipInstitute of Particle Physics, Canadaes_ES
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada (NSERC)es_ES
dc.description.sponsorshipCERNes_ES
dc.description.sponsorshipMinistry of Education, Youth & Sports - Czech Republic Czech Republic Governmentes_ES
dc.description.sponsorshipERDF, European Uniones_ES
dc.description.sponsorshipH2020-EU, European Uniones_ES
dc.description.sponsorshipMSCA, European Uniones_ES
dc.description.sponsorshipCentre National de la Recherche Scientifique (CNRS)es_ES
dc.description.sponsorshipFrench Atomic Energy Commissiones_ES
dc.description.sponsorshipIstituto Nazionale di Fisica Nucleare (INFN)es_ES
dc.description.sponsorshipPortuguese Foundation for Science and Technology European Commissiones_ES
dc.description.sponsorshipNRF, South Koreaes_ES
dc.description.sponsorshipComunidad de Madrides_ES
dc.description.sponsorshipLa Caixa Foundationes_ES
dc.description.sponsorshipSpanish Governmentes_ES
dc.description.sponsorshipState Secretariat for Education, Research and Innovation, Switzerlandes_ES
dc.description.sponsorshipSwiss National Science Foundation (SNSF)es_ES
dc.description.sponsorshipTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)es_ES
dc.description.sponsorshipRoyal Society of Londones_ES
dc.description.sponsorshipUKRI/STFC, United Kingdomes_ES
dc.description.sponsorshipUnited States Department of Energy (DOE)es_ES
dc.description.sponsorshipNational Science Foundation (NSF)es_ES
dc.language.isoenges_ES
dc.publisherAMER PHYSICAL SOCes_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.titleNeutrino interaction classification with a convolutional neural network in the DUNE far detectores_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1103/PhysRevD.102.092003


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España