Analysis of Fluorescence Telescope Data Using Machine Learning Methods
This work addresses data analysis challenges for fluorescence telescopes in cosmic ray studies, but it appears incremental as it applies existing methods to new data without claiming major breakthroughs.
The researchers tackled the problem of analyzing fluorescence telescope data to recognize extensive air shower tracks and reconstruct energy and arrival directions of primary particles, using model data from the EUSO-TA telescope with machine learning and neural networks, but no concrete results or numbers were reported.
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.