Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data

• Published in: Algorithms Vol. 16; no. 9; p. 448
• Main Authors: Zotov, Mikhail, Anzhiganov, Dmitry, Kryazhenkov, Aleksandr, Barghini, Dario, Battisti, Matteo, Belov, Alexander, Bertaina, Mario, Bianciotto, Marta, Bisconti, Francesca, Blaksley, Carl, Blin, Sylvie, Cambiè, Giorgio, Capel, Francesca, Casolino, Marco, Ebisuzaki, Toshikazu, Eser, Johannes, Fenu, Francesco, Franceschi, Massimo, Golzio, Alessio, Gorodetzky, Philippe, Kajino, Fumiyoshi, Kasuga, Hiroshi, Klimov, Pavel, Manfrin, Massimiliano, Marcelli, Laura, Miyamoto, Hiroko, Murashov, Alexey, Napolitano, Tommaso, Ohmori, Hiroshi, Olinto, Angela, Parizot, Etienne, Picozza, Piergiorgio, Piotrowski, Lech, Plebaniak, Zbigniew, Prévôt, Guillaume, Reali, Enzo, Ricci, Marco, Romoli, Giulia, Sakaki, Naoto, Shinozaki, Kenji, De La Taille, Christophe, Takizawa, Yoshiyuki, Vrábel, Michal, Wiencke, Lawrence
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023
• Subjects: Artificial neural networks; atmosphere; Collaboration; Cosmic rays; Evaluation; Experiments; Fluorescence; fluorescence telescope; International Space Station; Machine learning; meteor; Meteors; Meteors & meteorites; neural network; Neural networks; orbital experiment; pattern recognition; Radiation; Recognition; Space stations; Telescope; Telescopes; Ultraviolet radiation; UV illumination

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...