Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data
This work addresses signal recognition for fluorescence telescopes, potentially enabling onboard implementation in future orbital or balloon experiments, though it appears incremental.
The authors tackled the problem of recognizing meteor signals in Mini-EUSO telescope data by developing two simple artificial neural networks, achieving high accuracy in binary classification.
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.