abi, babakAcciarri, RobertoAcero O, Mario AAdamov, GAdams, DavidAdinolfi, MAhmad, ZSzczerbinska, Barbara2022-10-122022-10-122020-09-16B. Abi, B. Szczerbinska, et al. (DUNE Collaboration) Phys. Rev. D 102 https://doi.org/10.1103/PhysRevD.102.092003https://hdl.handle.net/1969.6/94078The 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-USAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/dunedeep underground neutrino experimentconvolutional neural networksneutrino oscillationsArticlehttps://doi.org/10.1103/PhysRevD.102.092003