TY  - JOUR
AU  - DUNE Collaboration (Abi, B. et al
AU  - Antonova, M.
AU  - Barenboim, G.
AU  - Cervera-Villanueva, A.
AU  - De Romeri, V.
AU  - Fernandez Menendez, P.
AU  - Garcia-Peris, M. A.
AU  - Izmaylov, A.
AU  - Martin-Albo, J.
AU  - Masud, M.
AU  - Mena, O.
AU  - Novella, P.
AU  - Sorel, M.
AU  - Ternes, C. A.
AU  - Tortola, M.
AU  - Valle, J. W. F.
PY  - 2020
DA  - 2020//
TI  - Neutrino interaction classification with a convolutional neural network in the DUNE far detector
T2  - Phys. Rev. D
JO  - Physical Review D
SP  - 092003
EP  - 20pp
VL  - 102
IS  - 9
PB  - Amer Physical Soc
AB  - 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.
SN  - 2470-0010
UR  - https://arxiv.org/abs/2006.15052
UR  - https://doi.org/10.1103/PhysRevD.102.092003
DO  - 10.1103/PhysRevD.102.092003
LA  - English
N1  - WOS:000587596500004
ID  - DUNECollaborationAbi_etal2020
ER  -