Neutrino interaction classification with a convolutional neural network in the DUNE far detector

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dc.creatorAbi, Babak
dc.creatorAcciarri, Roberto
dc.creatorAcero Ortega, Mario A.
dc.creatorAdamov, George
dc.creatorAdinolfi, Marco
dc.creatorAhmad‬, ‪Zulfequar
dc.creatorAhmad, Ali Junaid
dc.creatorAlion, T.
dc.creatorAlonso Monsalve, Saúl
dc.creatorAlt, C.
dc.creatorGomes, Ricardo Avelino
dc.date.accessioned2023-05-18T12:16:58Z
dc.date.available2023-05-18T12:16:58Z
dc.date.issued2020
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.pt_BR
dc.identifier.citationABI, B. et al. Neutrino interaction classification with a convolutional neural network in the DUNE far detector. Physical Review D, Washington, v. 102, n. 9, e092003, 2020. DOI: 10.1103/PhysRevD.102.092003. Disponível em: https://journals.aps.org/prd/abstract/10.1103/PhysRevD.102.092003. Acesso em: 16 maio 2023.pt_BR
dc.identifier.doie- 2470-0029
dc.identifier.doi10.1103/PhysRevD.102.092003
dc.identifier.issn2470-0010
dc.identifier.urihttp://repositorio.bc.ufg.br/handle/ri/22555
dc.language.isoengpt_BR
dc.publisher.countryEstados unidospt_BR
dc.publisher.departmentInstituto de Física - IF (RG)pt_BR
dc.rightsAcesso Abertopt_BR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleNeutrino interaction classification with a convolutional neural network in the DUNE far detectorpt_BR
dc.typeArtigopt_BR

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