Work

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

Year: 2024

Type: article

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 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.

Source: Moscow University Physics Bulletin

DOI: 10.3103/s0027134924702187

Author: Mikhail Zotov

Institution: Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University

Language: English

Topic: Astrophysics and Cosmic Phenomena

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funder: Russian Science Foundation

Awards: G1482197928, G80123820