Separation of Track- and Shower-like Energy Deposits in ProtoDUNE-SP Using a Convolutional Neural Network

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dc.authorid249433en_US
dc.contributor.authorBilki, Burak
dc.contributor.authorvd.
dc.date.accessioned2023-03-14T05:32:44Z
dc.date.available2023-03-14T05:32:44Z
dc.date.issued2022
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.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 electromag netic 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.en_US
dc.identifier.doi10.1140/epjc/s10052-022-10791-2
dc.identifier.issn0304-0941
dc.identifier.scopus2-s2.0-85139783137en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1140/epjc/s10052-022-10791-2
dc.identifier.wosWOS:000866503200001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGERen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.titleSeparation of Track- and Shower-like Energy Deposits in ProtoDUNE-SP Using a Convolutional Neural Networken_US
dc.typeArticleen_US

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