Analysis of Fluorescence Telescope Data Using Machine Learning Methods

TL;DR

This paper explores machine learning techniques to analyze fluorescence telescope data, aiming to improve the recognition and reconstruction of cosmic ray air showers, with potential applications to other telescopes.

Contribution

It introduces machine learning and neural network methods for analyzing fluorescence telescope data, focusing on track recognition and particle property reconstruction.

Findings

Machine learning can effectively identify air shower tracks.

Neural networks improve energy and direction reconstruction accuracy.

Potential for extending methods to other fluorescence telescopes.

Abstract

Fluorescence telescopes are among the key instruments used for studying ultra-high energy cosmic rays in all modern experiments. We use model data for a small ground-based telescope EUSO-TA to try some methods of machine learning and neural networks for recognizing tracks of extensive air showers in its data and for reconstruction of energy and arrival directions of primary particles. We also comment on the opportunities to use this approach for other fluorescence telescopes and outline possible ways of improving the performance of the suggested methods.