Analysis of Fluorescence Telescope Data Using Machine Learning Methods
Fuente:
arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866913928889499648 |
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| author | Zotov, Mikhail Zakharov, Pavel |
| author_facet | Zotov, Mikhail Zakharov, Pavel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_02311 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analysis of Fluorescence Telescope Data Using Machine Learning Methods Zotov, Mikhail Zakharov, Pavel Instrumentation and Methods for Astrophysics Machine Learning 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. |
| title | Analysis of Fluorescence Telescope Data Using Machine Learning Methods |
| topic | Instrumentation and Methods for Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2501.02311 |