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A Neural Network Approach for Selecting Track-Like Events in Fluorescence Telescope Data

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Bulletin of the Russian Academy of Sciences: Physics Aims and scope

Abstract

In 2016–2017, TUS, the world’s first experiment for testing the possibility of registering ultra-high energy cosmic rays (UHECRs) by their fluorescent radiation in the night atmosphere of Earth was carried out. Since 2019, the Russian-Italian fluorescence telescope (FT) Mini-EUSO (“UV Atmosphere”) has been operating on the ISS. The stratospheric experiment EUSO-SPB2, which will employ an FT for registering UHECRs, is planned for 2023. We show how simple convolutional neural networks can be effectively used to find track-like events in the variety of data obtained with such instruments.

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ACKNOWLEDGMENTS

The authors are grateful to the members of the Mini-EUSO experiment for numerous helpful discussions.

Funding

The study was supported by the Russian Science Foundation (project no. 22-22-00367).

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Authors and Affiliations

  1. Skobeltsyn Institute of Nuclear Physics, Moscow State University, 119991, Moscow, Russia

    M. Yu. Zotov

  2. Moscow State University, 119991, Moscow, Russia

    D. B. Sokolinskii

Authors
  1. M. Yu. Zotov
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  2. D. B. Sokolinskii
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on behalf of the JEM-EUSO Collaboration

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Correspondence to M. Yu. Zotov.

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The authors declare that they have no conflicts of interest.

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Zotov, M.Y., Sokolinskii, D.B. & on behalf of the JEM-EUSO Collaboration. A Neural Network Approach for Selecting Track-Like Events in Fluorescence Telescope Data. Bull. Russ. Acad. Sci. Phys. 87, 1049–1052 (2023). https://doi.org/10.3103/S1062873823702398

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  • DOI: https://doi.org/10.3103/S1062873823702398

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