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

dc.contributor.author Abi, B.
dc.contributor.author Acciarri, R.
dc.contributor.author Acero, M. A.
dc.contributor.author Adamov, G.
dc.contributor.author Adams, D.
dc.contributor.author Adinolfi, M.
dc.contributor.author Ahmad, Z.
dc.contributor.author Ahmed, J.
dc.contributor.author Alion, T.
dc.contributor.author Alonso Monsalve, S.
dc.contributor.author Alt, C.
dc.contributor.author Anderson, J.
dc.contributor.author Andreopoulos, C.
dc.contributor.author Andrews, M. P.
dc.contributor.author Andrianala, F.
dc.contributor.author Andringa, S.
dc.contributor.author Ankowski, A.
dc.contributor.author Antonova, M.
dc.contributor.author Antusch, S.
dc.contributor.author Aranda-Fernandez, A.
dc.contributor.author Ariga, A.
dc.contributor.author Arnold, L. O.
dc.contributor.author Arroyave, M. A.
dc.contributor.author Asaadi, J.
dc.contributor.author Aurisano, A.
dc.contributor.author Aushev, V.
dc.contributor.author Autiero, D.
dc.contributor.author Azfar, F.
dc.contributor.author Back, H.
dc.contributor.author Back, J. J.
dc.contributor.author Backhouse, C.
dc.contributor.author Baesso, P.
dc.contributor.author Bagby, L.
dc.contributor.author Bajou, R.
dc.contributor.author Balasubramanian, S.
dc.contributor.author Baldi, P.
dc.contributor.author Bambah, B.
dc.contributor.author Barao, F.
dc.contributor.author Barenboim, G.
dc.contributor.author Barker, G. J.
dc.contributor.author Barkhouse, W.
dc.contributor.author Barnes, C.
dc.contributor.author Barr, G.
dc.contributor.author Barranco Monarca, J.
dc.contributor.author Barros, N.
dc.contributor.author Barrow, J. L.
dc.contributor.author Bashyal, A.
dc.contributor.author Basque, V.
dc.contributor.author Bay, F.
dc.contributor.author Bazo Alba, J. L.
dc.contributor.author Beacom, J. F.
dc.contributor.author Bechetoille, E.
dc.contributor.author Behera, B.
dc.contributor.author Bellantoni, L.
dc.contributor.author Bellettini, G.
dc.contributor.author Bellini, V.
dc.contributor.author Beltramello, O.
dc.contributor.author Belver, D.
dc.contributor.author Benekos, N.
dc.contributor.author Bento Neves, F.
dc.contributor.author Berger, J.
dc.contributor.author Berkman, S.
dc.contributor.author Bernardini, P.
dc.contributor.author Berner, R. M.
dc.contributor.author Berns, H.
dc.contributor.author Bertolucci, S.
dc.contributor.author Betancourt, M.
dc.contributor.author Bezawada, Y.
dc.contributor.author Bhattacharjee, M.
dc.contributor.author Bhuyan, B.
dc.contributor.author Biagi, S.
dc.contributor.author Bian, J.
dc.contributor.author Biassoni, M.
dc.contributor.author Biery, K.
dc.contributor.author Bilki, B.
dc.contributor.author Bishai, M.
dc.contributor.author Bitadze, A.
dc.contributor.author Blake, A.
dc.contributor.author Blanco Siffert, B.
dc.contributor.author Blaszczyk, F. D.M.
dc.contributor.author Blazey, G. C.
dc.contributor.author Blucher, E.
dc.contributor.author Boissevain, J.
dc.contributor.author Bolognesi, S.
dc.contributor.author Bolton, T.
dc.contributor.author Bonesini, M.
dc.contributor.author Bongrand, M.
dc.contributor.author Bonini, F.
dc.contributor.author Booth, A.
dc.contributor.author Booth, C.
dc.contributor.author Bordoni, S.
dc.contributor.author Borkum, A.
dc.contributor.author Boschi, T.
dc.contributor.author Bostan, N.
dc.contributor.author Bour, P.
dc.contributor.author Boyd, S. B.
dc.contributor.author Boyden, D.
dc.contributor.author Bracinik, J.
dc.contributor.author Braga, D.
dc.contributor.author Brailsford, D.
dc.date.accessioned 2022-03-27T11:38:30Z
dc.date.available 2022-03-27T11:38:30Z
dc.date.issued 2020-11-09
dc.description.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.
dc.identifier.citation Physical Review D. v.102(9)
dc.identifier.issn 24700010
dc.identifier.uri 10.1103/PhysRevD.102.092003
dc.identifier.uri https://link.aps.org/doi/10.1103/PhysRevD.102.092003
dc.identifier.uri https://dspace.uohyd.ac.in/handle/1/14338
dc.title Neutrino interaction classification with a convolutional neural network in the DUNE far detector
dc.type Journal. Article
dspace.entity.type
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