Publication: Application of Deep Learning Techniques for Multiparticle Track Reconstruction of Drift Chamber Data
Дата
2021
Авторы
Vorob'ev, V. S.
Zadeba, E. A.
Nikolaenko, R. V.
Petrukhin, A. A.
Troshin, I. Y.
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Аннотация
© 2021, Pleiades Publishing, Ltd.Abstract: The new coordinate-tracking detector TREK based on drift chambers is being developed at National Research Nuclear University MEPhI to study inclined extensive air showers. To reconstruct the events with a high multiplicity from the data of drift chambers, the histogram method, which is designed to search for parallel tracks, is currently used. However, we observe afterpulses in the experimental data obtained using a coordinate-tracking unit based on drift chambers (CTUDC). The afterpulses lead to fake track reconstructions. To solve this problem, a new method is being developed using deep learning. This paper presents the results of the development of this method and its application to simulated data.
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Application of Deep Learning Techniques for Multiparticle Track Reconstruction of Drift Chamber Data / Vorob'ev, V.S. [et al.] // Physics of Atomic Nuclei. - 2021. - 84. - № 9. - P. 1567-1571. - 10.1134/S1063778821090350
URI
https://www.doi.org/10.1134/S1063778821090350
https://www.scopus.com/record/display.uri?eid=2-s2.0-85124889791&origin=resultslist
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000757854800007
https://openrepository.mephi.ru/handle/123456789/25263
https://www.scopus.com/record/display.uri?eid=2-s2.0-85124889791&origin=resultslist
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000757854800007
https://openrepository.mephi.ru/handle/123456789/25263