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arXiv · 2408.02440

Reconstruction of energy and arrival directions of UHECRs registered by fluorescence telescopes with a neural network

Mikhail Zotov
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Abstract

Fluorescence telescopes are important instruments widely used in modern experiments for registering ultraviolet radiation from extensive air showers (EASs) generated by cosmic rays of ultra-high energies. We present a proof-of-concept convolutional neural network aimed at reconstruction of energy and arrival directions of primary particles using model data for two telescopes developed by the international JEM-EUSO collaboration. We also demonstrate how a simple convolutional encoder-decoder can be used for EAS track recognition. The approach is generic and can be adopted for other fluorescence telescopes.

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Mikhail Zotov. 2024-08-05. Reconstruction of energy and arrival directions of UHECRs registered by fluorescence telescopes with a neural network. https://doi.org/10.3103/s0027134924702187

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