- Main
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.102.092003Abstract
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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