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

publication date

  • November 2020

start page

  • 092003-1

end page

  • 092003-20


  • 9


  • 102

International Standard Serial Number (ISSN)

  • 2470-0010


  • 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.


  • Computer Science
  • Telecommunications


  • neutrino interactions; neutrino oscillations; neutrinos; neutrino detectors