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



Abi, B, Acciarri, R, Acero, MA, Adamov, G, Adams, D, Adinolfi, M, Ahmad, Z, Ahmed, J, Alion, T, Monsalve, S Alonso
et al (show 965 more authors) (2020) Neutrino interaction classification with a convolutional neural network in the DUNE far detector. PHYSICAL REVIEW D, 102 (9). 092003-.

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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.

Item Type: Article
Additional Information: 39 pages, 11 figures
Uncontrolled Keywords: physics.ins-det, physics.ins-det, hep-ex
Divisions: Faculty of Science and Engineering > School of Physical Sciences
Depositing User: Symplectic Admin
Date Deposited: 07 Sep 2021 15:51
Last Modified: 18 Jan 2023 23:18
DOI: 10.1103/PhysRevD.102.092003
Open Access URL: https://journals.aps.org/prd/abstract/10.1103/Phys...
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3110206