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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;
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- Ariga, A;
- Arnold, LO;
- Arroyave, MA;
- Asaadi, J;
- Aurisano, A;
- Aushev, V;
- Autiero, D;
- Azfar, F;
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- Siffert, B Blanco;
- Blaszczyk, FDM;
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- Zalesak, J;
- Zambelli, L;
- Zamorano, B;
- Zani, A;
- Zazueta, L;
- Zeller, GP;
- Zennamo, J;
- Zeug, K;
- Zhang, C;
- Zhao, M;
- Zhivun, E;
- Zhu, G;
- Zimmerman, ED;
- Zito, M;
- Zucchelli, S;
- Zuklin, J;
- Zutshi, V;
- Zwaska, R
- et al.
Published Web Location
https://doi.org/10.1103/physrevd.102.092003Abstract
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.
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