Analysis of Fluorescence Telescope Data Using Machine Learning Methods

• URL: http://arxiv.org/abs/2501.02311v1
• Date: Sat, 04 Jan 2025 15:20:09 GMT
• Title: Analysis of Fluorescence Telescope Data Using Machine Learning Methods
• Authors: Mikhail Zotov, Pavel Zakharov,
• Abstract summary: We use model data for a small ground-based telescope to try some methods of machine learning and neural networks for recognizing tracks of extensive air showers in its data. 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.
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• 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.

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