Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data-Reference-Cited by-同舟云学术

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

Published:2023-09-19 Issue:9 Volume:16 Page:448
ISSN:1999-4893
Container-title:Algorithms
language:en
Short-container-title:Algorithms

Author:

Zotov Mikhail¹^ORCID,Anzhiganov Dmitry¹²,Kryazhenkov Aleksandr¹²,Barghini Dario³⁴⁵,Battisti Matteo³^ORCID,Belov Alexander¹⁶^ORCID,Bertaina Mario³⁴^ORCID,Bianciotto Marta⁴^ORCID,Bisconti Francesca³⁷,Blaksley Carl⁸,Blin Sylvie⁹,Cambiè Giorgio⁷¹⁰,Capel Francesca¹¹¹²,Casolino Marco⁷⁸¹⁰^ORCID,Ebisuzaki Toshikazu⁸,Eser Johannes¹³^ORCID,Fenu Francesco⁴,Franceschi Massimo¹⁴,Golzio Alessio³⁴^ORCID,Gorodetzky Philippe⁹,Kajino Fumiyoshi¹⁵,Kasuga Hiroshi⁸,Klimov Pavel¹⁶^ORCID,Manfrin Massimiliano³⁴,Marcelli Laura⁷^ORCID,Miyamoto Hiroko³,Murashov Alexey¹⁶,Napolitano Tommaso¹⁴,Ohmori Hiroshi⁸,Olinto Angela¹³,Parizot Etienne⁹¹⁶,Picozza Piergiorgio⁷¹⁰,Piotrowski Lech¹⁷,Plebaniak Zbigniew³⁴¹⁸,Prévôt Guillaume⁹,Reali Enzo⁷¹⁰^ORCID,Ricci Marco¹⁴,Romoli Giulia⁷¹⁰,Sakaki Naoto⁸,Shinozaki Kenji¹⁸,De La Taille Christophe¹⁹,Takizawa Yoshiyuki⁸,Vrábel Michal¹⁸,Wiencke Lawrence²⁰

Affiliation:

1. Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow 119991, Russia

2. Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow 119991, Russia

3. INFN, Sezione di Torino, Via Pietro Giuria, 1, 10125 Torino, Italy

4. Dipartimento di Fisica, Università di Torino, Via Pietro Giuria, 1, 10125 Torino, Italy

5. INAF, Osservatorio Astrofisico di Torino, Via Osservatorio 20, Pino Torinese, 10025 Torino, Italy

6. Faculty of Physics, M.V. Lomonosov Moscow State University, Moscow 119991, Russia

7. INFN, Sezione di Roma Tor Vergata, Via della Ricerca Scientifica 1, 00133 Roma, Italy

8. RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan

9. AstroParticule et Cosmologie, CNRS, Université Paris Cité, F-75013 Paris, France

10. Dipartimento di Fisica, Universita degli Studi di Roma Tor Vergata, Via della Ricerca Scientifica 1, 00133 Roma, Italy

11. Max Planck Institute for Physics, Föhringer Ring 6, D-80805 Munich, Germany

12. Department of Particle and Astroparticle Physics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden

13. Department of Astronomy and Astrophysics, The University of Chicago, Chicago, IL 60637, USA

14. INFN—Laboratori Nazionali di Frascati, 00044 Frascati, Italy

15. Department of Physics, Konan University, Kobe 658-8501, Japan

16. AstroParticule et Cosmologie, Institut Universitaire de France (IUF), CEDEX 05, 75231 Paris, France

17. Faculty of Physics, University of Warsaw, 02-093 Warsaw, Poland

18. National Centre for Nuclear Research, Ul. Pasteura 7, PL-02-093 Warsaw, Poland

19. Omega, Ecole Polytechnique, CNRS/IN2P3, 91128 Palaiseau, France

20. Department of Physics, Colorado School of Mines, Golden, CO 80401, USA

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.

Funder

Russian Science Foundation

Publisher

MDPI AG

Subject

Computational Mathematics,Computational Theory and Mathematics,Numerical Analysis,Theoretical Computer Science

Link

https://www.mdpi.com/1999-4893/16/9/448/pdf

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