Reconstruction of Energy and Arrival Directions of UHECRs Registered by Fluorescence Telescopes with Neural Networks

Fluorescence telescopes are important instruments widely used in modern experiments for registering ultraviolet radiation from extensive air showers (EASs) generated by cosmic rays of ultrahigh energies. We present proof-of-concept convolutional neural networks 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.