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


Abi B., Acciarri R., Acero M., Adamov G., Adams D., Adinolfi M., ...More

Physical Review D, vol.102, no.9, 2020 (SCI-Expanded, Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 102 Issue: 9
  • Publication Date: 2020
  • Doi Number: 10.1103/physrevd.102.092003
  • Journal Name: Physical Review D
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, INSPEC, zbMATH
  • Marmara University Affiliated: No

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.