Neutrino interaction classification with a convolutional neural network in the DUNE far detector
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2020
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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.
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ABI, B. et al. Neutrino interaction classification with a convolutional neural network in the DUNE far detector. Physical Review D, Washington, v. 102, n. 9, e092003, 2020. DOI: 10.1103/PhysRevD.102.092003. Disponível em: https://journals.aps.org/prd/abstract/10.1103/PhysRevD.102.092003. Acesso em: 16 maio 2023.