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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Published in Physical Review D, Volume 102, Issue 9, 2020, pages 092003-.
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Abi, B., Acciarri, R., Acero, M. A., Adamov, G., Adams, D., Adinolfi, M., Ahmad, Z., Ahmed, J., Alion, T., Alonso Monsalve, S., Alt, C., Anderson, J., Andreopoulos, C., Andrews, M. P., Andrianala, F., Andringa, S., Ankowski, A., Antonova, M., Antusch, S., … Zwaska, R. (2020). Neutrino interaction classification with a convolutional neural network in the dune far detector. Physical Review D, 102(9). https://doi.org/10.1103/physrevd.102.092003