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Multiparticle Event Reconstruction Using Deep Learning Methods for Coordinate-Tracking Unit Based on Drift Chambers

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dc.contributor.authorVorob'ev, V. S.
dc.contributor.authorZadeba, E. A.
dc.contributor.authorNikolaenko, R. V.
dc.contributor.authorPetrukhin, A. A.
dc.contributor.authorTroshin, I. Y.
dc.contributor.authorВоробьев, Владислав Станиславович
dc.contributor.authorЗадеба, Егор Александрович
dc.contributor.authorНиколаенко, Роман Владимирович
dc.contributor.authorПетрухин, Анатолий Афанасьевич
dc.contributor.authorТрошин, Иван Юрьевич
dc.date.accessioned2024-11-30T02:30:45Z
dc.date.available2024-11-30T02:30:45Z
dc.date.issued2021
dc.description.abstract© 2021, Pleiades Publishing, Ltd.Abstract: The new coordinate-tracking detector TREK based on multiwire drift chambers is being developed in the National Research Nuclear University MEPhI to study the muon component of extensive air showers. Its prototype named the coordinate-tracking unit based on drift chambers (CTUDC) has been designed. Investigation of the multiparticle events registered by the unit has shown all the complexity of reconstruction of such events. The analytical reconstruction methods applied earlier demonstrate their inefficacy in dealing with these events. A new approach based on deep learning methods is being developed to solve this problem. The paper presents the results of application of artificial neural networks to experimental data obtained by the CTUDC.
dc.format.extentС. 1780-1788
dc.identifier.citationMultiparticle Event Reconstruction Using Deep Learning Methods for Coordinate-Tracking Unit Based on Drift Chambers / Vorob'ev, V.S. [et al.] // Physics of Atomic Nuclei. - 2021. - 84. - № 10. - P. 1780-1788. - 10.1134/S1063778821090362
dc.identifier.doi10.1134/S1063778821090362
dc.identifier.urihttps://www.doi.org/10.1134/S1063778821090362
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85125346494&origin=resultslist
dc.identifier.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000757854700020
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/25280
dc.relation.ispartofPhysics of Atomic Nuclei
dc.titleMultiparticle Event Reconstruction Using Deep Learning Methods for Coordinate-Tracking Unit Based on Drift Chambers
dc.typeArticle
dspace.entity.typePublication
oaire.citation.issue10
oaire.citation.volume84
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