Neutrino interaction classification with a convolutional neural network in the DUNE far detector
Physical Review D
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.
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.; and Antusch, S., "Neutrino interaction classification with a convolutional neural network in the DUNE far detector" (2020). NIU Bibliography. 667.
Department of Physics