Neutrino interaction classification with a convolutional neural network in the DUNE far detector
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Abi, 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.
SponsorshipFermi Research Alliance, LLC (FRA) DE-AC02-07CH11359; National Council for Scientific and Technological Development (CNPq); Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro (FAPERJ); FAPEG, Brazil; Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP); Canada Foundation for Innovation; Institute of Particle Physics, Canada; Natural Sciences and Engineering Research Council of Canada (NSERC); CERN; Ministry of Education, Youth & Sports - Czech Republic Czech Republic Government; ERDF, European Union; H2020-EU, European Union; MSCA, European Union; Centre National de la Recherche Scientifique (CNRS); French Atomic Energy Commission; Istituto Nazionale di Fisica Nucleare (INFN); Portuguese Foundation for Science and Technology European Commission; NRF, South Korea; Comunidad de Madrid; La Caixa Foundation; Spanish Government; State Secretariat for Education, Research and Innovation, Switzerland; Swiss National Science Foundation (SNSF); Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK); Royal Society of London; UKRI/STFC, United Kingdom; United States Department of Energy (DOE); National Science Foundation (NSF)
The 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.