Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
dc.creator | Abud, Adam Abed | |
dc.creator | Abi, Babak | |
dc.creator | Acciarri, Roberto | |
dc.creator | Acero Ortega, Mario A. | |
dc.creator | Adames, Márcio Rostirolla | |
dc.creator | Adamov, George | |
dc.creator | Adamowski, M. | |
dc.creator | Adams, David | |
dc.creator | Adinolfi, Marco | |
dc.creator | Aduszkiewicz, Antoni | |
dc.creator | Gomes, Ricardo Avelino | |
dc.date.accessioned | 2023-05-19T12:32:51Z | |
dc.date.available | 2023-05-19T12:32:51Z | |
dc.date.issued | 2022 | |
dc.description.abstract | Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neu trino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article pro poses an algorithm based on a convolutional neural network to perform the classification of energy deposits and recon structed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experi mental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation. | pt_BR |
dc.identifier.citation | ABUD, A. Abed et al. Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network. European Physical Journal C. Particles And Fields, Berlim, v. 82, e903, 2022. Disponível em: https://link-springer-com.ez49.periodicos.capes.gov.br/article/10.1140/epjc/s10052-022-10791-2. Acesso em: 16 maio 2023. | pt_BR |
dc.identifier.issn | 1434-604 | |
dc.identifier.issn | e- 1434-6052 | |
dc.identifier.uri | http://repositorio.bc.ufg.br/handle/ri/22560 | |
dc.language.iso | eng | pt_BR |
dc.publisher.country | Alemanha | pt_BR |
dc.publisher.department | Instituto de Física - IF (RG) | pt_BR |
dc.rights | Acesso Aberto | pt_BR |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network | pt_BR |
dc.type | Artigo | pt_BR |
Arquivos
Pacote Original
1 - 1 de 1
Nenhuma Miniatura disponível
- Nome:
- Artigo - Adam Abed Abud - 2022.pdf
- Tamanho:
- 3.16 MB
- Formato:
- Adobe Portable Document Format
- Descrição:
Licença do Pacote
1 - 1 de 1
Nenhuma Miniatura disponível
- Nome:
- license.txt
- Tamanho:
- 1.71 KB
- Formato:
- Item-specific license agreed upon to submission
- Descrição: