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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Neutrino interaction classification with a convolutional neural network in the DUNE far detector
AU - DUNE Collaboration
AU - Collaboration, DUNE
AU - Arnold, L. O.
AU - Arroyave, M. A.
AU - Asaadi, J.
AU - Aurisano, A.
AU - Aushev, V.
AU - Autiero, D.
AU - Azfar, F.
AU - Back, H.
AU - Back, J. J.
AU - Backhouse, C.
AU - Baesso, P.
AU - Bagby, L.
AU - Bajou, R.
AU - Balasubramanian, S.
AU - Baldi, P.
AU - Bambah, B.
AU - Barao, F.
AU - Barenboim, G.
AU - Barker, G. J.
AU - Barkhouse, W.
AU - Barnes, C.
AU - Barr, G.
AU - Monarca, J. Barranco
AU - Barros, N.
AU - Blake, A.
AU - Brailsford, D.
AU - Cross, R.
AU - Nowak, J. A.
AU - Ratoff, P.
PY - 2020/11/9
Y1 - 2020/11/9
N2 - 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.
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.
KW - physics.ins-det
KW - hep-ex
U2 - 10.1103/PhysRevD.102.092003
DO - 10.1103/PhysRevD.102.092003
M3 - Journal article
VL - 102
JO - Physical Review D
JF - Physical Review D
SN - 1550-7998
M1 - 092003
ER -