Neutrino interaction classification with a convolutional neural network in the DUNE far detector
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 a...
- Autores:
-
Acero, M. A.
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad del Atlántico
- Repositorio:
- Repositorio Uniatlantico
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniatlantico.edu.co:20.500.12834/963
- Acceso en línea:
- https://hdl.handle.net/20.500.12834/963
- Palabra clave:
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc/4.0/
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Neutrino interaction classification with a convolutional neural network in the DUNE far detector |
| title |
Neutrino interaction classification with a convolutional neural network in the DUNE far detector |
| spellingShingle |
Neutrino interaction classification with a convolutional neural network in the DUNE far detector |
| title_short |
Neutrino interaction classification with a convolutional neural network in the DUNE far detector |
| title_full |
Neutrino interaction classification with a convolutional neural network in the DUNE far detector |
| title_fullStr |
Neutrino interaction classification with a convolutional neural network in the DUNE far detector |
| title_full_unstemmed |
Neutrino interaction classification with a convolutional neural network in the DUNE far detector |
| title_sort |
Neutrino interaction classification with a convolutional neural network in the DUNE far detector |
| dc.creator.fl_str_mv |
Acero, M. A. |
| dc.contributor.author.none.fl_str_mv |
Acero, M. A. |
| description |
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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2020 |
| dc.date.issued.none.fl_str_mv |
2020-11-10 |
| dc.date.submitted.none.fl_str_mv |
2020-06-26 |
| dc.date.accessioned.none.fl_str_mv |
2022-11-15T21:15:43Z |
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2022-11-15T21:15:43Z |
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Artículo |
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https://hdl.handle.net/20.500.12834/963 |
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10.48550/arXiv.2006.15052 Focus to learn more |
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Universidad del Atlántico |
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Repositorio Universidad del Atlántico |
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https://hdl.handle.net/20.500.12834/963 |
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10.48550/arXiv.2006.15052 Focus to learn more Universidad del Atlántico Repositorio Universidad del Atlántico |
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eng |
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eng |
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Attribution-NonCommercial 4.0 International |
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Sede Norte |
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Cornell University |
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Acero, M. A.979f9c6a-faae-415d-b017-8ea1e9afa74c2022-11-15T21:15:43Z2022-11-15T21:15:43Z2020-11-102020-06-26https://hdl.handle.net/20.500.12834/96310.48550/arXiv.2006.15052 Focus to learn moreUniversidad del AtlánticoRepositorio Universidad del AtlánticoThe 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.application/pdfenghttp://creativecommons.org/licenses/by-nc/4.0/Attribution-NonCommercial 4.0 Internationalinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Cornell UniversityNeutrino interaction classification with a convolutional neural network in the DUNE far detectorPúblico generalinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1BarranquillaSede Norte[1] Z. Maki, M. Nakagawa, and S. Sakata, Prog. Theor. Phys. 28, 870 (1962)[2] B. Pontecorvo, Sov. Phys. JETP 26, 984 (1968), [Zh. Eksp. Teor. Fiz. 53, 1717 (1967)].[3] Y. Fukuda et al. (Super-Kamiokande), Phys. Rev. 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