@misc{10481/67204,
year = {2020},
url = {http://hdl.handle.net/10481/67204},
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.},
organization = {Fermi Research Alliance, LLC (FRA) 

DE-AC02-07CH11359},
organization = {National Council for Scientific and Technological Development (CNPq)},
organization = {Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro (FAPERJ)},
organization = {FAPEG, Brazil},
organization = {Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)},
organization = {Canada Foundation for Innovation},
organization = {Institute of Particle Physics, Canada},
organization = {Natural Sciences and Engineering Research Council of Canada (NSERC)},
organization = {CERN},
organization = {Ministry of Education, Youth & Sports - Czech Republic

Czech Republic Government},
organization = {ERDF, European Union},
organization = {H2020-EU, European Union},
organization = {MSCA, European Union},
organization = {Centre National de la Recherche Scientifique (CNRS)},
organization = {French Atomic Energy Commission},
organization = {Istituto Nazionale di Fisica Nucleare (INFN)},
organization = {Portuguese Foundation for Science and Technology

European Commission},
organization = {NRF, South Korea},
organization = {Comunidad de Madrid},
organization = {La Caixa Foundation},
organization = {Spanish Government},
organization = {State Secretariat for Education, Research and Innovation, Switzerland},
organization = {Swiss National Science Foundation (SNSF)},
organization = {Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)},
organization = {Royal Society of London},
organization = {UKRI/STFC, United Kingdom},
organization = {United States Department of Energy (DOE)},
organization = {National Science Foundation (NSF)},
publisher = {AMER PHYSICAL SOC},
title = {Neutrino interaction classification with a convolutional neural network in the DUNE far detector},
doi = {10.1103/PhysRevD.102.092003},
author = {García Gámez, Diego and Zamorano García, Bruno and Dune Collaboration},
}