Neutrino interaction classification with a convolutional neural network in the DUNE far detector García Gámez, Diego Zamorano García, Bruno Dune Collaboration 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. 2021-03-15T08:59:49Z 2021-03-15T08:59:49Z 2020 info:eu-repo/semantics/article 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. http://hdl.handle.net/10481/67204 10.1103/PhysRevD.102.092003 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España AMER PHYSICAL SOC