Neutrino interaction classification with a convolutional neural network in the DUNE far detector
DUNE Collaboration. (2020). Neutrino interaction classification with a convolutional neural network in the DUNE far detector. Physical Review D, 102(9), Article 092003. https://doi.org/10.1103/PhysRevD.102.092003
Published inPhysical Review D
© Authors, 2020
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.
PublisherAmerican Physical Society
Publication in research information system
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Additional information about fundingThis work wassupported 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/IN2P3and CEA, France; INFN, Italy; FCT, Portugal; NRF, SouthKorea; Comunidad de Madrid, Fundación“La Caixa”andMICINN, Spain; State Secretariat for Education, Researchand Innovation and SNSF, Switzerland; TÜBİTAK,Turkey; The Royal Society and UKRI/STFC, UnitedKingdom; DOE and NSF, United States of America. ...
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