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

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Arquitectura de Computadores, Comunicaciones y Sistemases
dc.contributor.authorAbi, B.
dc.contributor.authorAlonso Monsalve, Saúl
dc.contributor.authorGarcía Carballeira, Félix
dc.contributor.otherDUNE
dc.date.accessioned2023-07-14T08:46:38Z
dc.date.available2023-07-14T08:46:38Z
dc.date.issued2020-11-01
dc.descriptionDocumento escrito por un elevado número de autores/as, solo se referencia el/la que aparece en primer lugar y los/as autores/as pertenecientes a la UC3M.es
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.en
dc.description.sponsorshipThis document was prepared by the DUNE Collaboration using the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359. This work was supported by CNPq, FAPERJ, FAPEG and FAPESP, Brazil; CFI, Institute of Particle Physics and NSERC, Canada; CERN; MŠMT, Czech Republic; ERDF, H2020-EU and MSCA, European Union; CNRS/IN2P3 and CEA, France; INFN, Italy; FCT, Portugal; NRF, South Korea; Comunidad de Madrid, Fundación "La Caixa" and MICINN, Spain; State Secretariat for Education, Research and Innovation and SNSF, Switzerland; TÜBITAK, Turkey; The Royal Society and UKRI/STFC, United Kingdom; DOE and NSF, United States of America.en
dc.format.extent20
dc.identifier.bibliographicCitationAbi, B., et al. (2020). Neutrino interaction classification with a convolutional neural network in the dune far detector. Physical review D, 102(9), 092003.en
dc.identifier.doihttps://doi.org/10.1103/PhysRevD.102.092003
dc.identifier.issn2470-0010
dc.identifier.publicationfirstpage092003-1
dc.identifier.publicationissue9
dc.identifier.publicationlastpage092003-20
dc.identifier.publicationtitlePhysical review Den
dc.identifier.publicationvolume102
dc.identifier.urihttps://hdl.handle.net/10016/37844
dc.identifier.uxxiAR/0000027714
dc.language.isoeng
dc.publisherAPS
dc.rights© The Authors, 2020.en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaInformáticaes
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherNeutrino interactionsen
dc.subject.otherNeutrino oscillationsen
dc.subject.otherNeutrinosen
dc.subject.otherNeutrino detectorsen
dc.titleNeutrino interaction classification with a convolutional neural network in the DUNE far detectoren
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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