PT Journal
AU DUNE Collaboration (Abi, Bea
   Antonova, M
   Barenboim, G
   Cervera-Villanueva, A
   De Romeri, V
   Fernandez Menendez, P
   Garcia-Peris, MA
   Izmaylov, A
   Martin-Albo, J
   Masud, M
   Mena, O
   Novella, P
   Sorel, M
   Ternes, CA
   Tortola, M
   Valle, JWF
TI Neutrino interaction classification with a convolutional neural network in the DUNE far detector
SO Physical Review D
JI Phys. Rev. D
PY 2020
BP 092003
EP 20pp
VL 102
IS 9
DI 10.1103/PhysRevD.102.092003
LA English
AB 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.
ER