Kavli Affiliate: Angela Olinto
| First 5 Authors: Mikhail Zotov, Dmitry Anzhiganov, Aleksandr Kryazhenkov, Dario Barghini, Matteo Battisti
Mini-EUSO is a wide-angle fluorescence telescope that registers ultraviolet
(UV) radiation in the nocturnal atmosphere of Earth from the International
Space Station. Meteors are among multiple phenomena that manifest themselves
not only in the visible range but also in the UV. We present two simple
artificial neural networks that allow for recognizing meteor signals in the
Mini-EUSO data with high accuracy in terms of a binary classification problem.
We expect that similar architectures can be effectively used for signal
recognition in other fluorescence telescopes, regardless of the nature of the
signal. Due to their simplicity, the networks can be implemented in onboard
electronics of future orbital or balloon experiments.
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