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

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Title: Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network.
Authors: Abed Abud, A.1,2 (AUTHOR), Abi, B.3 (AUTHOR), Acciarri, R.4 (AUTHOR), Acero, M. A.5 (AUTHOR), Adames, M. R.6 (AUTHOR), Adamov, G.7 (AUTHOR), Adamowski, M.4 (AUTHOR), Adams, D.8 (AUTHOR), Adinolfi, M.9 (AUTHOR), Aduszkiewicz, A.10 (AUTHOR), Aguilar, J.11 (AUTHOR), Ahmad, Z.12 (AUTHOR), Ahmed, J.13 (AUTHOR), Aimard, B.14 (AUTHOR), Ali-Mohammadzadeh, B.15,16 (AUTHOR), Alion, T.17 (AUTHOR), Allison, K.18 (AUTHOR), Alonso Monsalve, S.1,19 (AUTHOR), AlRashed, M.20 (AUTHOR), Alt, C.19 (AUTHOR)
Source: European Physical Journal C -- Particles & Fields. Oct2022, Vol. 82 Issue 10, p1-19. 19p.
Subjects: Convolutional neural networks, Liquid argon, Neutrinos
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 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. [ABSTRACT FROM AUTHOR]
Copyright of European Physical Journal C -- Particles & Fields is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network.
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  Data: <searchLink fieldCode="JN" term="%22European+Physical+Journal+C+--+Particles+%26+Fields%22">European Physical Journal C -- Particles & Fields</searchLink>. Oct2022, Vol. 82 Issue 10, p1-19. 19p.
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  Data: 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 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of European Physical Journal C -- Particles & Fields is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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