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Neutrino interaction classification with a convolutional neural network in the DUNE far detector

  • Author(s): Abi, B;
  • Acciarri, R;
  • Acero, MA;
  • Adamov, G;
  • Adams, D;
  • Adinolfi, M;
  • Ahmad, Z;
  • Ahmed, J;
  • Alion, T;
  • Alonso Monsalve, S;
  • Alt, C;
  • Anderson, J;
  • Andreopoulos, C;
  • Andrews, MP;
  • Andrianala, F;
  • Andringa, S;
  • Ankowski, A;
  • Antonova, M;
  • Antusch, S;
  • Aranda-Fernandez, A;
  • Ariga, A;
  • Arnold, LO;
  • Arroyave, MA;
  • Asaadi, J;
  • Aurisano, A;
  • Aushev, V;
  • Autiero, D;
  • Azfar, F;
  • Back, H;
  • Back, JJ;
  • Backhouse, C;
  • Baesso, P;
  • Bagby, L;
  • Bajou, R;
  • Balasubramanian, S;
  • Baldi, P;
  • Bambah, B;
  • Barao, F;
  • Barenboim, G;
  • Barker, GJ;
  • Barkhouse, W;
  • Barnes, C;
  • Barr, G;
  • Barranco Monarca, J;
  • Barros, N;
  • Barrow, JL;
  • Bashyal, A;
  • Basque, V;
  • Bay, F;
  • Bazo Alba, JL;
  • Beacom, JF;
  • Bechetoille, E;
  • Behera, B;
  • Bellantoni, L;
  • Bellettini, G;
  • Bellini, V;
  • Beltramello, O;
  • Belver, D;
  • Benekos, N;
  • Bento Neves, F;
  • Berger, J;
  • Berkman, S;
  • Bernardini, P;
  • Berner, RM;
  • Berns, H;
  • Bertolucci, S;
  • Betancourt, M;
  • Bezawada, Y;
  • Bhattacharjee, M;
  • Bhuyan, B;
  • Biagi, S;
  • Bian, J;
  • Biassoni, M;
  • Biery, K;
  • Bilki, B;
  • Bishai, M;
  • Bitadze, A;
  • Blake, A;
  • Blanco Siffert, B;
  • Blaszczyk, FDM;
  • Blazey, GC;
  • Blucher, E;
  • Boissevain, J;
  • Bolognesi, S;
  • Bolton, T;
  • Bonesini, M;
  • Bongrand, M;
  • Bonini, F;
  • Booth, A;
  • Booth, C;
  • Bordoni, S;
  • Borkum, A;
  • Boschi, T;
  • Bostan, N;
  • Bour, P;
  • Boyd, SB;
  • Boyden, D;
  • Bracinik, J;
  • Braga, D;
  • Brailsford, D
  • et al.

Published Web Location

https://doi.org/10.1103/PhysRevD.102.092003
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.

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