Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

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dc.creatorAbud, Adam Abed
dc.creatorAbi, Babak
dc.creatorAcciarri, Roberto
dc.creatorAcero Ortega, Mario A.
dc.creatorAdames, Márcio Rostirolla
dc.creatorAdamov, George
dc.creatorAdamowski, M.
dc.creatorAdams, David
dc.creatorAdinolfi, Marco
dc.creatorAduszkiewicz, Antoni
dc.creatorGomes, Ricardo Avelino
dc.date.accessioned2023-05-19T12:32:51Z
dc.date.available2023-05-19T12:32:51Z
dc.date.issued2022
dc.description.abstractLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neu trino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article pro poses an algorithm based on a convolutional neural network to perform the classification of energy deposits and recon structed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experi mental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation.pt_BR
dc.identifier.citationABUD, A. Abed et al. Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network. European Physical Journal C. Particles And Fields, Berlim, v. 82, e903, 2022. Disponível em: https://link-springer-com.ez49.periodicos.capes.gov.br/article/10.1140/epjc/s10052-022-10791-2. Acesso em: 16 maio 2023.pt_BR
dc.identifier.issn1434-604
dc.identifier.issne- 1434-6052
dc.identifier.urihttp://repositorio.bc.ufg.br/handle/ri/22560
dc.language.isoengpt_BR
dc.publisher.countryAlemanhapt_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.titleSeparation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural networkpt_BR
dc.typeArtigopt_BR

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