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.
Publication Source (Journal or Book title)
Physical Review D
Abi, B., Acciarri, R., Acero, M., Adamov, G., Adams, D., Adinolfi, M., Ahmad, Z., Ahmed, J., Alion, T., Alonso Monsalve, S., Alt, C., Anderson, J., Andreopoulos, C., Andrews, M., Andrianala, F., Andringa, S., Ankowski, A., Antonova, M., Antusch, S., Aranda-Fernandez, A., Ariga, A., Arnold, L., Arroyave, M., Asaadi, J., Aurisano, A., Aushev, V., Autiero, D., & Azfar, F. (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