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Convolutional neural network approach to event position reconstruction in DarkSide-50 experiment

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dc.contributor.authorGrobov, A.
dc.contributor.authorIlyasov, A.
dc.contributor.authorИльясов, Айдар Иршатович
dc.date.accessioned2024-11-27T15:44:43Z
dc.date.available2024-11-27T15:44:43Z
dc.date.issued2020
dc.description.abstract© 2020 Institute of Physics Publishing. All rights reserved.Neural networks are currently used in various fields of science and technology, as well as in experiments related to particle physics. DarkSide-50 is a two-phase (liquid and gas) argon TPC which has two main signals: scintillation in LAr (S1 signal) and electroluminescence in GAr (S2 signal) [1]. In the low-mass dark matter search only the more energetic second signal is used for position reconstruction [2]. As a result only events detected by seven central photomultiplier tubes are used for the analysis. Here we attempt to improve reconstruction using the convolutional neural networks (CNN).
dc.identifier.citationGrobov, A. Convolutional neural network approach to event position reconstruction in DarkSide-50 experiment / Grobov, A., Ilyasov, A. // Journal of Physics: Conference Series. - 2020. - 1690. - № 1. - 10.1088/1742-6596/1690/1/012013
dc.identifier.doi10.1088/1742-6596/1690/1/012013
dc.identifier.urihttps://www.doi.org/10.1088/1742-6596/1690/1/012013
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85098770460&origin=resultslist
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/23009
dc.relation.ispartofJournal of Physics: Conference Series
dc.titleConvolutional neural network approach to event position reconstruction in DarkSide-50 experiment
dc.typeConference Paper
dspace.entity.typePublication
oaire.citation.issue1
oaire.citation.volume1690
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relation.isAuthorOfPublication.latestForDiscovery0bc3941f-8c6f-409f-8685-75d5e187201e
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