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

0537337 - FZÚ 2021 RIV US eng J - Journal Article
Abi, B. - Acciarri, R. - Acero, M. A. - Lokajíček, Miloš - Zálešák, Jaroslav - Zuklín, Josef … Total 975 authors
Neutrino interaction classification with a convolutional neural network in the DUNE far detector.
Physical Review D. Roč. 102, č. 9 (2020), s. 1-20, č. článku 092003. ISSN 2470-0010. E-ISSN 2470-0029
Research Infrastructure: Fermilab-CZ II - 90113
Institutional support: RVO:68378271
Keywords : DUNE * CP violation * neutrino/mu * particle identification
OECD category: Particles and field physics
Impact factor: 5.296, year: 2020
Method of publishing: Open access

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.

Permanent Link: http://hdl.handle.net/11104/0315059