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

被引：2

作者：

Zotov, Mikhail ^{[1
]}

Anzhiganov, Dmitry ^{[1
,2
]}

Kryazhenkov, Aleksandr ^{[1
,2
]}

Barghini, Dario ^{[3
,4
,5
]}

Battisti, Matteo ^{[3
]}

Belov, Alexander ^{[1
,6
]}

Bertaina, Mario ^{[3
,4
]}

Bianciotto, Marta ^{[4
]}

Bisconti, Francesca ^{[3
,7
]}

Blaksley, Carl ^{[8
]}

Blin, Sylvie ^{[9
]}

Cambie, Giorgio ^{[7
,10
]}

Capel, Francesca ^{[11
,12
]}

Casolino, Marco ^{[7
,8
,10
]}

Ebisuzaki, Toshikazu ^{[8
]}

Eser, Johannes ^{[13
]}

Fenu, Francesco ^{[4
,21
]}

Franceschi, Massimo Alberto ^{[14
]}

Golzio, Alessio ^{[3
,4
]}

Gorodetzky, Philippe ^{[9
]}

Kajino, Fumiyoshi ^{[15
]}

Kasuga, Hiroshi ^{[8
]}

Klimov, Pavel ^{[1
,6
]}

Manfrin, Massimiliano ^{[3
,4
]}

Marcelli, Laura ^{[7
]}

Miyamoto, Hiroko ^{[3
]}

Murashov, Alexey ^{[1
,6
]}

Napolitano, Tommaso ^{[14
]}

Ohmori, Hiroshi ^{[8
]}

Olinto, Angela ^{[13
]}

Parizot, Etienne ^{[9
,16
]}

Picozza, Piergiorgio ^{[7
,10
]}

Piotrowski, Lech Wiktor ^{[17
]}

Plebaniak, Zbigniew ^{[3
,4
,18
]}

Prevot, Guillaume ^{[9
]}

Reali, Enzo ^{[7
,10
]}

Ricci, Marco ^{[14
]}

Romoli, Giulia ^{[7
,10
]}

Sakaki, Naoto ^{[8
]}

Shinozaki, Kenji ^{[18
]}

De La Taille, Christophe ^{[19
]}

Takizawa, Yoshiyuki ^{[8
]}

Vrabel, Michal ^{[18
]}

Wiencke, Lawrence ^{[20
]}

Werner, Frank

机构：

[1] Lomonosov Moscow State Univ, Skobeltsyn Inst Nucl Phys, Moscow 119991, Russia

[2] Lomonosov Moscow State Univ, Fac Computat Math & Cybernet, Moscow 119991, Russia

[3] INFN, Sez Torino, Via Pietro Giuria 1, I-10125 Turin, Italy

[4] Univ Torino, Dipartimento Fis, Via Pietro Giuria 1, I-10125 Turin, Italy

[5] INAF, Osservatorio Astrofisico Torino, Via Osservatorio 20, I-10025 Turin, Italy

[6] Moscow MV Lomonosov State Univ, Fac Phys, Moscow 119991, Russia

[7] INFN, Sez Roma Tor Vergata, Via Ric Sci 1, I-00133 Rome, Italy

[8] RIKEN, 2-1 Hirosawa, Wako, Saitama 3510198, Japan

[9] Univ Paris Cite, AstroParticule & Cosmol, CNRS, F-75013 Paris, France

[10] Univ Roma Tor Vergata, Dipartimento Fis, Via Ric Sci 1, I-00133 Rome, Italy

[11] Max Planck Inst Phys & Astrophys, Fohringer Ring 6, D-80805 Munich, Germany

[12] KTH Royal Inst Technol, Dept Particle & Astroparticle Phys, SE-10044 Stockholm, Sweden

[13] Univ Chicago, Dept Astron & Astrophys, Chicago, IL 60637 USA

[14] INFN, Lab Nazl Frascati, I-00044 Frascati, Italy

[15] Konan Univ, Dept Phys, Kobe 6588501, Japan

[16] Inst Univ France IUF, AstroParticule & Cosmol, F-75231 Paris 05, France

[17] Univ Warsaw, Fac Phys, PL-02093 Warsaw, Poland

[18] Natl Ctr Nucl Res, Ul Pasteura 7, PL-02093 Warsaw, Poland

[19] Ecole Polytech, Omega, CNRS, IN2P3, F-91128 Palaiseau, France

[20] Colorado Sch Mines, Dept Phys, Golden, CO 80401 USA

[21] Agenzia Spaziale Italiana, Via Politecn, I-00133 Rome, Italy

来源：

ALGORITHMS | 2023年 / 16卷 / 09期

基金：

俄罗斯科学基金会;

关键词：

machine learning; neural network; pattern recognition; meteor; fluorescence telescope; orbital experiment; UV illumination; atmosphere; PERFORMANCE;

D O I：

10.3390/a16090448

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页数：14

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