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

TL;DR

This paper demonstrates a neural network approach for reconstructing the energy and arrival directions of ultra-high-energy cosmic rays using fluorescence telescope data, also enabling EAS track recognition.

Contribution

It introduces a convolutional neural network method for energy and direction reconstruction in fluorescence telescope data, applicable to various instruments.

Findings

Neural network accurately reconstructs cosmic ray properties.

Encoder-decoder effectively recognizes EAS tracks.

Method is adaptable to different fluorescence telescopes.

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.