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Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
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SYSNO ASEP 0563954 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network Author(s) Abed Abud, A. (CH)
Abi, B. (GB)
Acciarri, R. (US)
Filip, Peter (FZU-D) ORCID
Kvasnička, Jiří (FZU-D) RID, ORCID
Lokajíček, Miloš (FZU-D) RID, ORCID
Pěč, Viktor (FZU-D) ORCID
Zálešák, Jaroslav (FZU-D) RID, ORCID
Zuklín, Josef (FZU-D) ORCIDNumber of authors 1228 Article number 903 Source Title European Physical Journal C. - : Springer - ISSN 1434-6044
Roč. 82, č. 10 (2022)Number of pages 19 s. Language eng - English Country DE - Germany Keywords DUNE ; neural network ; efficiency ; performance Subject RIV BF - Elementary Particles and High Energy Physics OECD category Particles and field physics Research Infrastructure Fermilab-CZ II - 90113 - Fyzikální ústav AV ČR, v. v. i. Method of publishing Open access Institutional support FZU-D - RVO:68378271 UT WOS 000866503200001 EID SCOPUS 85139783137 DOI 10.1140/epjc/s10052-022-10791-2 Annotation 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. Workplace Institute of Physics Contact Kristina Potocká, potocka@fzu.cz, Tel.: 220 318 579 Year of Publishing 2023 Electronic address https://hdl.handle.net/11104/0335738
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