@article{3193369,
    title = "Neutrino interaction classification with a convolutional neural network
in the DUNE far detector",
    author = "Abi, B. and Acciarri, R. and Acero, M. A. and Adamov, G. and Adams, D. and and Adinolfi, M. and Ahmad, Z. and Ahmed, J. and Alion, T. and Monsalve, and S. Alonso and Alt, C. and Anderson, J. and Andreopoulos, C. and Andrews, and M. P. and Andrianala, F. and Andringa, S. and Ankowski, A. and Antonova, and M. and Antusch, S. and Aranda-Fernandez, A. and Ariga, A. and Arnold, L. and O. and Arroyave, M. A. and Asaadi, J. and Aurisano, A. and Aushev, V. and and Autiero, D. and Azfar, F. and Back, H. and Back, J. J. and and Backhouse, C. and Baesso, P. and Bagby, L. and Bajou, R. and and Balasubramanian, S. and Baldi, P. and Bambah, B. and Barao, F. and and Barenboim, G. and Barker, G. J. and Barkhouse, W. and Barnes, C. and and Barr, G. and Monarca, J. Barranco and Barros, N. and Barrow, J. L. and and Bashyal, A. and Basque, V. and Bay, F. and Alba, J. L. Bazo and Beacom, and J. F. and Bechetoille, E. and Behera, B. and Bellantoni, L. and and Bellettini, G. and Bellini, V. and Beltramello, O. and Belver, D. and and Benekos, N. and Bento Neves, F. and Berger, J. and Berkman, S. and and Bernardini, P. and Berner, R. M. and Berns, H. and Bertolucci, S. and and Betancourt, M. and Bezawada, Y. and Bhattacharjee, M. and Bhuyan, B. and and Biagi, S. and Bian, J. and Biassoni, M. and Biery, K. and Bilki, B. and and Bishai, M. and Bitadze, A. and Blake, A. and Blanco Siffert, B. and and Blaszczyk, F. D. M. and Blazey, G. C. and Blucher, E. and Boissevain, J. and and Bolognesi, S. and Bolton, T. and Bonesini, M. and Bongrand, M. and and Bonini, F. and Booth, A. and Booth, C. and Bordoni, S. and Borkum, A. and and Boschi, T. and Bostan, N. and Bour, P. and Boyd, S. B. and Boyden, and D. and Bracinik, J. and Braga, D. and Brailsford, D. and Brandt, A. and and Bremer, J. and Brew, C. and Brianne, E. and Brice, S. J. and Brizzolari, and C. and Bromberg, C. and Brooijmans, G. and Brooke, J. and Bross, A. and and Brunetti, G. and Buchanan, N. and Budd, H. and Caiulo, D. and Calafiura, and P. and Calcutt, J. and Calin, M. and Calvez, S. and Calvo, E. and and Camilleri, L. and Caminata, A. and Campanelli, M. and Caratelli, D. and and Carini, G. and Carlus, B. and Carniti, P. and Terrazas, I. Caro and and Carranza, H. and Castillo, A. and Castromonte, C. and Cattadori, C. and and Cavalier, F. and Cavanna, F. and Centro, S. and Cerati, G. and Cervelli, and A. and Cervera Villanueva, A. and Chalifour, M. and Chang, C. and and Chardonnet, E. and Chatterjee, A. and Chattopadhyay, S. and Chaves, J. and and Chen, H. and Chen, M. and Chen, Y. and Cherdack, D. and Chi, C. and and Childress, S. and Chiriacescu, A. and Cho, K. and Choubey, S. and and Christensen, A. and Christian, D. and Christodoulou, G. and Church, E. and and Clarke, P. and Coan, T. E. and Cocco, A. G. and Coelho, J. A. 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J. and Wolcott, J. and and Wongjirad, T. and Wood, K. and Wood, L. and Worcester, E. and Worcester, and M. and Wret, C. and Wu, W. and Wu, W. and Xiao, Y. and Yang, G. and and Yang, T. and Yershov, N. and Yonehara, K. and Young, T. and Yu, B. and and Yu, J. and Zaki, R. and Zalesak, J. and Zambelli, L. and Zamorano, B. and and Zani, A. and Zazueta, L. and Zeller, G. P. and Zennamo, J. and Zeug, and K. and Zhang, C. and Zhao, M. and Zhivun, E. and Zhu, G. and Zimmerman, and E. D. and Zito, M. and Zucchelli, S. and Zuklin, J. and Zutshi, V. and and Zwaska, R. and DUNE Collaboration",
    journal = "Physical Review D",
    year = "2020",
    volume = "102",
    number = "9",
    publisher = "AMER PHYSICAL SOC",
    issn = "2470-0010, 2470-0029",
    doi = "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."
}