Analysis of Fluorescence Telescope Data Using Machine Learning Methods

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 to recognize tracks of extensive air showers in its data and to reconstruct energy and arrival directions of primary particles. We also comment on the opportunities to use this approach for other fluorescence telescopes and outline opportunities to further improve the performance of the suggested methods.