Reconstruction of energy and arrival directions of UHECRs registered by fluorescence telescopes with a neural network
arXiv:2408.02440 · doi:10.3103/S0027134924702187
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.
10 pages; presented at the DLCP-2024 conference; v2: minor wording changes to address reviewer's comments
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