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 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 electromagetic 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.
Digital Object Identifier (DOI)
Published in The European Physical Journal C, Volume 82, 2022.
© The Author(s) 2022
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecomm ons.org/licenses/by/4.0/
Abed Abud, A., Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Aduszkiewicz, A., Aguilar, J., Ahmad, Z., Ahmed, J., Aimard, B., Ali-Mohammadzadeh, B., Alion, T., Allison, K., Alonso Monsalve, S., AlRashed, M., & Alt, C. (2022). Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network. The European Physical Journal C, 82. https://doi.org/10.1140/epjc/s10052-022-10791-2