Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data
Instrumentation and Methods for Astrophysics
2023-11-28 v1 Computer Vision and Pattern Recognition
Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.2311.14983,
title = {Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data},
author = {Mikhail Zotov and Dmitry Anzhiganov and Aleksandr Kryazhenkov and Dario Barghini and Matteo Battisti and Alexander Belov and Mario Bertaina and Marta Bianciotto and Francesca Bisconti and Carl Blaksley and Sylvie Blin and Giorgio Cambiè and Francesca Capel and Marco Casolino and Toshikazu Ebisuzaki and Johannes Eser and Francesco Fenu and Massimo Alberto Franceschi and Alessio Golzio and Philippe Gorodetzky and Fumiyoshi Kajino and Hiroshi Kasuga and Pavel Klimov and Massimiliano Manfrin and Laura Marcelli and Hiroko Miyamoto and Alexey Murashov and Tommaso Napolitano and Hiroshi Ohmori and Angela Olinto and Etienne Parizot and Piergiorgio Picozza and Lech Wiktor Piotrowski and Zbigniew Plebaniak and Guillaume Prévôt and Enzo Reali and Marco Ricci and Giulia Romoli and Naoto Sakaki and Kenji Shinozaki and Christophe De La Taille and Yoshiyuki Takizawa and Michal Vrábel and Lawrence Wiencke},
journal= {arXiv preprint arXiv:2311.14983},
year = {2023}
}
Comments
15 pages