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

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dc.contributor.authorabi, babak
dc.contributor.authorAcciarri, Roberto
dc.contributor.authorAcero O, Mario A
dc.contributor.authorAdamov, G
dc.contributor.authorAdams, David
dc.contributor.authorAdinolfi, M
dc.contributor.authorAhmad, Z
dc.contributor.authorSzczerbinska, Barbara
dc.creator.orcidhttp://orcid.org/0000-0003-4477-4350en_US
dc.creator.orcidhttp://orcid.org/0000-0003-4477-4350
dc.date.accessioned2022-10-12T21:02:12Z
dc.date.available2022-10-12T21:02:12Z
dc.date.issued2020-09-16
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_US
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; TÜBİTAK, Turkey; The Royal Society and UKRI/STFC, United Kingdom; DOE and NSF, United States of America.en_US
dc.identifier.citationB. Abi, B. Szczerbinska, et al. (DUNE Collaboration) Phys. Rev. D 102 https://doi.org/10.1103/PhysRevD.102.092003en_US
dc.identifier.doihttps://doi.org/10.1103/PhysRevD.102.092003
dc.identifier.urihttps://hdl.handle.net/1969.6/94078
dc.language.isoen_USen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectduneen_US
dc.subjectdeep underground neutrino experimenten_US
dc.subjectconvolutional neural networksen_US
dc.subjectneutrino oscillationsen_US
dc.titleNeutrino interaction classification with convolutional neural network in the DUNE far detectoren_US
dc.typeArticleen_US

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