Neutrino interaction classification with a convolutional neural network in the DUNE far detector
Acero, Mario A.
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Abi, B., Acciarri, R., Acero, M. A., Adamov, G., Adams, D., Adinolfi, M., . . . Zwaska, R. (2020). Neutrino interaction classification with a convolutional neural network in the DUNE far detector. Physical Review D, 102(9) doi:10.1103/PhysRevD.102.092003
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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