Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
Nenhuma Miniatura disponível
Data
2022
Título da Revista
ISSN da Revista
Título de Volume
Editor
Resumo
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.
Descrição
Palavras-chave
Citação
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.