Neural Network Based Approach to Recognition of Meteor Tracks in the
  Mini-EUSO Telescope Data
Mikhail Zotov Dmitry Anzhiganov Aleksandr Kryazhenkov Dario Barghini Matteo Battisti Alexander Belov Mario Bertaina Marta Bianciotto Francesca Bisconti Carl Blaksley Sylvie Blin Giorgio Cambiè Francesca Capel Marco Casolino Toshikazu Ebisuzaki Johannes Eser Francesco Fenu Massimo Alberto Franceschi Alessio Golzio Philippe Gorodetzky Fumiyoshi Kajino Hiroshi Kasuga Pavel Klimov Massimiliano Manfrin Laura Marcelli Hiroko Miyamoto Alexey Murashov Tommaso Napolitano Hiroshi Ohmori Angela Olinto Etienne Parizot Piergiorgio Picozza Lech Wiktor Piotrowski Zbigniew Plebaniak Guillaume Prévôt Enzo Reali Marco Ricci Giulia Romoli Naoto Sakaki Kenji Shinozaki Christophe De La Taille Yoshiyuki Takizawa Michal Vrábel Lawrence Wiencke
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.