%0 Journal Article
%T Neutrino interaction classification with a convolutional neural network in the DUNE far detector
%A DUNE Collaboration (Abi, B. et al
%A Antonova, M.
%A Barenboim, G.
%A Cervera-Villanueva, A.
%A De Romeri, V.
%A Fernandez Menendez, P.
%A Garcia-Peris, M. A.
%A Izmaylov, A.
%A Martin-Albo, J.
%A Masud, M.
%A Mena, O.
%A Novella, P.
%A Sorel, M.
%A Ternes, C. A.
%A Tortola, M.
%A Valle, J. W. F.
%J Physical Review D
%D 2020
%V 102
%N 9
%I Amer Physical Soc
%@ 2470-0010
%G English
%F DUNECollaborationAbi_etal2020
%O WOS:000587596500004
%O exported from refbase (https://references.ific.uv.es/refbase/show.php?record=4598), last updated on Tue, 24 Nov 2020 09:40:50 +0000
%X 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.
%R 10.1103/PhysRevD.102.092003
%U https://arxiv.org/abs/2006.15052
%U https://doi.org/10.1103/PhysRevD.102.092003
%P 092003-20pp