Neural Networks for Searching for Meteoral Signals in the Data of the Orbital Telescope “UV Atmosphere”

M. Zotov; Зотов М.; D. Sokolinskii; Соколинский Д.; A. Arifullin; Арифуллин А.

doi:10.31857/S0004629923060105

Neural Networks for Searching for Meteoral Signals in the Data of the Orbital Telescope “UV Atmosphere”

Autores: Zotov M.¹, Sokolinskii D.², Arifullin A.²
Afiliações:
1. Skobeltsyn Institute of Nuclear Physics, Moscow State University
2. Moscow State University
Edição: Volume 100, Nº 6 (2023)
Páginas: 557-566
Seção: Articles
URL: https://journals.rcsi.science/0004-6299/article/view/139095
DOI: https://doi.org/10.31857/S0004629923060105
EDN: https://elibrary.ru/VBIJUW
ID: 139095

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Resumo

Since 2019, the Russian–Italian experiment “UV Atmosphere” (Mini-EUSO) has been operational on the International Space Station. The primary instrument of this experiment is a wide-angle telescope positioned toward nadir. Its main objective is to generate an ultraviolet map of the Earth’s nocturnal atmosphere radiation. This map serves as a crucial element in the preparation of a large-scale experiment involving the study of extremely high-energy cosmic rays using an orbiting telescope. Similar to the preceding TUS experiment, the “UV Atmosphere” instrument detects signals from various atmospheric processes in the ultraviolet range, including the luminosity of meteors. In this paper, we describe two simple neural networks that effectively extract meteor signals from the overall data stream. The proposed approach can also be applied to identify track-like signals of various origins in the data obtained from fluorescent and Cherenkov telescopes.

Palavras-chave

telescope “UV Atmosphere”, neural networks, pattern recognition, meteors

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