Speaker
Mikhail Zotov
(Lomonosov Moscow State University)
Description
Fluorescence telescopes are important instruments widely used in modern experiments for registering ultraviolet radiation from extensive air showers (EASs) generated by cosmic rays of ultra-high energies. We present a proof-of-concept convolutional neural network aimed at reconstruction of energy and arrival directions of primary particles using model data for two telescopes developed by the international JEM-EUSO collaboration. We also demonstrate how a simple convolutional encoder-decoder can be used for EAS track recognition. The approach is generic and can be adopted for other fluorescence telescopes.
| Type of Contribution | talk |
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Author
Mikhail Zotov
(Lomonosov Moscow State University)
