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