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

Abi, B. and Acciarri, R. and Acero, M. A. and Adamov, G. and Adams, D. and Adinolfi, M. and Ahmad, Z. and Ahmed, J. and Alion, T. and Alonso Monsalve, S. and Alt, C. and Anderson, J. and Andreopoulos, C. and Andrews, M. P. and Andrianala, F. and Andringa, S. and Ankowski, A. and Antonova, M. and Antusch, S. and Aranda-Fernandez, A. and Ariga, A. and Arnold, L. O. and Arroyave, M. A. and Asaadi, J. and Aurisano, A. and Aushev, V. and Autiero, D. and Azfar, F. and Back, H. and Back, J. J. and Backhouse, C. and Baesso, P. and Bagby, L. and Bajou, R. and Balasubramanian, S. and Baldi, P. and Bambah, B. and Barao, F. and Barenboim, G. and Barker, G. J. and Barkhouse, W. and Barnes, C. and Barr, G. and Barranco Monarca, J. and Barros, N. and Barrow, J. L. and Bashyal, A. and Basque, V. and Bay, F. and Bazo Alba, J. L. and Beacom, J. F. and Bechetoille, E. and Behera, B. and Bellantoni, L. and Bellettini, G. and Bellini, V. and Beltramello, O. and Belver, D. and Benekos, N. and Bento Neves, 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Zucchelli, S. and Zuklin, J. and Zutshi, V. and Zwaska, R. (2020) Neutrino interaction classification with a convolutional neural network in the DUNE far detector. Physical Review D, 102 (9). Art. No. 092003. ISSN 2470-0010.

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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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Alonso Monsalve, S.0000-0002-9678-7121
Mualem, L.0000-0003-3716-5100
Necib, L.0000-0003-2806-1414
Patterson, R. B.0000-0002-5787-9517
Whitehead, L. H.0000-0002-3327-2534
Additional Information:© 2020 Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Received 15 June 2020; accepted 16 September 2020; published 9 November 2020. This document was prepared by the DUNE Collaboration using the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359. This work was supported by CNPq, FAPERJ, FAPEG and FAPESP, Brazil; CFI, Institute of Particle Physics and NSERC, Canada; CERN; MŠMT, Czech Republic; ERDF, H2020-EU and MSCA, European Union; CNRS/IN2P3 and CEA, France; INFN, Italy; FCT, Portugal; NRF, South Korea; Comunidad de Madrid, Fundación “La Caixa” and MICINN, Spain; State Secretariat for Education, Research and Innovation and SNSF, Switzerland; TÜBİTAK, Turkey; The Royal Society and UKRI/STFC, United Kingdom; DOE and NSF, United States of America.
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Deposited By: Tony Diaz
Deposited On:09 Nov 2020 22:04
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