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arXiv, 2022

DOI: 10.48550/arxiv.2203.17053

SpringerOpen, The European Physical Journal C, 10(82), 2022

DOI: 10.1140/epjc/s10052-022-10791-2

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Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Journal article published in 2022 by H. de Souza, Marcelo Augusto Leigui de Oliveira, Flor de Maria Blaszczyk, Helio da Motta, Leonardo da Silva Peres, Andre de Gouvea, Pedro de Holanda, Iker de Icaza Astiz, Paul de Jong, J. R. T. de Mello Neto, Greg de Souza, Antonio Verdugo de Osa, E. Batista das Chagas, M. Leigui de Oliveira, H. Vieira de Souza and other authors.
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

AbstractLiquid 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 neutrino 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 proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental 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.