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

doi:10.3390/a16090448

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

École Polytechnique

Moscow State University
INFN Sezione di Torino
University of Turin
Istituto di Astrofisica e Planetologia Spaziali (IAPS)
INFN Roma Tor Vergata
Riken
Astroparticule and Cosmol APC
University of Rome “Tor Vergata”
Max-Planck-Institut für Physik
KTH Royal Institute of Technology
University of Chicago
LNF-INFN
Konan University
Institut Universitaire de France
University of Warsaw
National Centre for Nuclear Research (NCBJ)
Colorado School of Mines

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

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.

Original language	English
Article number	448
Journal	Algorithms
Volume	16
Issue number	9
DOIs	https://doi.org/10.3390/a16090448
Publication status	Published - 1 Sept 2023

Keywords

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

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10.3390/a16090448

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Zotov, M., Anzhiganov, D., Kryazhenkov, A., Barghini, D., Battisti, M., Belov, A., Bertaina, M., Bianciotto, M., Bisconti, F., Blaksley, C., Blin, S., Cambiè, G., Capel, F., Casolino, M., Ebisuzaki, T., Eser, J., Fenu, F., Franceschi, M. A., Golzio, A., ... Wiencke, L. (2023). Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data. Algorithms, 16(9), Article 448. https://doi.org/10.3390/a16090448

@article{b66e9606ffe3428a82eac22358939e9b,

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

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 = "UV illumination, atmosphere, fluorescence telescope, machine learning, meteor, neural network, orbital experiment, pattern recognition",

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 Cambi{\`e} and Francesca Capel and Marco Casolino and Toshikazu Ebisuzaki and Johannes Eser and Francesco Fenu and Franceschi, \{Massimo Alberto\} 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 Piotrowski, \{Lech Wiktor\} and Zbigniew Plebaniak and Guillaume Pr{\'e}v{\^o}t and Enzo Reali and Marco Ricci and Giulia Romoli and Naoto Sakaki and Kenji Shinozaki and \{De La Taille\}, Christophe and Yoshiyuki Takizawa and Michal Vr{\'a}bel and Lawrence Wiencke",

note = "Publisher Copyright: {\textcopyright} 2023 by the authors.",

year = "2023",

month = sep,

day = "1",

doi = "10.3390/a16090448",

language = "English",

volume = "16",

journal = "Algorithms",

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Zotov, M, Anzhiganov, D, Kryazhenkov, A, Barghini, D, Battisti, M, Belov, A, Bertaina, M, Bianciotto, M, Bisconti, F, Blaksley, C, Blin, S, Cambiè, G, Capel, F, Casolino, M, Ebisuzaki, T, Eser, J, Fenu, F, Franceschi, MA, Golzio, A, Gorodetzky, P, Kajino, F, Kasuga, H, Klimov, P, Manfrin, M, Marcelli, L, Miyamoto, H, Murashov, A, Napolitano, T, Ohmori, H, Olinto, A, Parizot, E, Picozza, P, Piotrowski, LW, Plebaniak, Z, Prévôt, G, Reali, E, Ricci, M, Romoli, G, Sakaki, N, Shinozaki, K, De La Taille, C, Takizawa, Y, Vrábel, M & Wiencke, L 2023, 'Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data', Algorithms, vol. 16, no. 9, 448. https://doi.org/10.3390/a16090448

TY - JOUR

T1 - Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data

AU - Zotov, Mikhail

AU - Anzhiganov, Dmitry

AU - Kryazhenkov, Aleksandr

AU - Barghini, Dario

AU - Battisti, Matteo

AU - Belov, Alexander

AU - Bertaina, Mario

AU - Bianciotto, Marta

AU - Bisconti, Francesca

AU - Blaksley, Carl

AU - Blin, Sylvie

AU - Cambiè, Giorgio

AU - Capel, Francesca

AU - Casolino, Marco

AU - Ebisuzaki, Toshikazu

AU - Eser, Johannes

AU - Fenu, Francesco

AU - Franceschi, Massimo Alberto

AU - Golzio, Alessio

AU - Gorodetzky, Philippe

AU - Kajino, Fumiyoshi

AU - Kasuga, Hiroshi

AU - Klimov, Pavel

AU - Manfrin, Massimiliano

AU - Marcelli, Laura

AU - Miyamoto, Hiroko

AU - Murashov, Alexey

AU - Napolitano, Tommaso

AU - Ohmori, Hiroshi

AU - Olinto, Angela

AU - Parizot, Etienne

AU - Picozza, Piergiorgio

AU - Piotrowski, Lech Wiktor

AU - Plebaniak, Zbigniew

AU - Prévôt, Guillaume

AU - Reali, Enzo

AU - Ricci, Marco

AU - Romoli, Giulia

AU - Sakaki, Naoto

AU - Shinozaki, Kenji

AU - De La Taille, Christophe

AU - Takizawa, Yoshiyuki

AU - Vrábel, Michal

AU - Wiencke, Lawrence

PY - 2023/9/1

Y1 - 2023/9/1

N2 - 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.

AB - 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.

KW - UV illumination

KW - atmosphere

KW - fluorescence telescope

KW - machine learning

KW - meteor

KW - neural network

KW - orbital experiment

KW - pattern recognition

UR - https://www.scopus.com/pages/publications/85172246630

U2 - 10.3390/a16090448

DO - 10.3390/a16090448

M3 - Article

AN - SCOPUS:85172246630

SN - 1999-4893

VL - 16

JO - Algorithms

JF - Algorithms

IS - 9

M1 - 448

ER -

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

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