Published November 1, 2020 | Version Published + Submitted
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Neutrino interaction classification with a convolutional neural network in the DUNE far detector

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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.

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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Published - PhysRevD.102.092003.pdf

Submitted - 2006.15052.pdf

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2006.15052.pdf

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Identifiers

Eprint ID
106514
Resolver ID
CaltechAUTHORS:20201109-130221740

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Funding

Department of Energy (DOE)
DE-AC02-07CH11359
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ)
Fundação de Amparo à Pesquisa do Estado de Goiás (FAPEG)
Fundação de Amparo à Pesquisa do Estado de Sao Paulo (FAPESP)
Canada Foundation for Innovation
Institute of Particle Physics
Natural Sciences and Engineering Research Council of Canada (NSERC)
CERN
Ministry of Education, Youth and Sports (Czech Republic)
European Regional Development Funds (ERDF)
Marie Curie Fellowship
European Union
Centre National de la Recherche Scientifique (CNRS)
Institut National de Physique Nucléaire et de Physique des Particules (IN2P3)
Commissariat à l'énergie atomique (CEA)
Istituto Nazionale di Fisica Nucleare (INFN)
Fundação para a Ciência e a Tecnologia (FCT)
National Research Foundation of Korea
Comunidad de Madrid
La Caixa Foundation
Ministerio de Ciencia e Innovación (MCINN)
State Secretariat for Education, Research and Innovation (SER)
Swiss National Science Foundation (SNSF)
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK)
Royal Society
UK Research and Innovation
Science and Technology Facilities Council (STFC)
NSF

Dates

Created
2020-11-09
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Updated
2021-11-16
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