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

https://doi.org/10.56304/S2079562920060615

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.

About the Authors

V. S. Vorob’ev
National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)
Russian Federation

Moscow, 115409



Е. А. Zadeba
National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)
Russian Federation

Moscow, 115409



R. V. Nikolaenko
National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)
Russian Federation

Moscow, 115409



А. А. Petrukhin
National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)
Russian Federation

Moscow, 115409



I. Yu. Troshin
National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)
Russian Federation

Moscow, 115409 



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Review

For citations:


Vorob’ev V.S., Zadeba Е.А., Nikolaenko R.V., Petrukhin А.А., Troshin I.Yu. Multiparticle Event Reconstruction Using Deep Learning Methods for Coordinate-Tracking Unit Based on Drift Chambers. Nuclear Physics and Engineering. 2021;12(5):289-297. (In Russ.) https://doi.org/10.56304/S2079562920060615

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ISSN 2079-5629 (Print)
ISSN 2079-5637 (Online)