# Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data
## 1 Introduction  
The JEM-EUSO (Joint Exploratory Missions for Extreme Universe Space Observatory) collaboration is developing a program of studying ultra-high energy cosmic rays (UHECRs) with a wide angle telescope from a low Earth orbit [1; 2; 3]. The idea is based on the possibility to register fluorescence and Cherenkov radiation in the ultraviolet (UV) range that is emitted during development of extensive air showers generated by primary particles hitting the atmosphere [4]. There are several benefits of this technique in comparison with ground-based experiments: (i) it can provide a huge exposure necessary for collecting sufficient statistics of these extremely rare events; (ii) the celestial sphere can be observed almost uniformly, which is important for anisotropy studies; and (iii) the whole sky can be observed with one instrument. It became clear at early stages of the development of the JEM-EUSO program that an orbital telescope aimed at studying UHECRs can serve as a tool for exploring other phenomena that manifest themselves in the UV range in the nocturnal atmosphere of Earth [5]. It was demonstrated by TUS, the world's first orbital fluorescence telescope aimed for testing the technique of studying UHECRs from space, that such an instrument can provide data on transient luminous events, thunderstorm activity, meteors, anthropogenic illumination of different kinds, and other types of signals [6; 7]. In particular, observations of meteors are considered as an important branch of studies in the JEM-EUSO program [8; 9]. The JEM-EUSO program is being implemented in a number of steps aimed at development and testing of different aspects of a full-blown orbital experiment. In particular, laser shots were successfully registered by a fluorescence telescope looking down on the atmosphere within the EUSO-Balloon mission [10]. A wide program of studies is being performed with the EUSO-TA experiment [11]. In 2018-2019, the Mini-EUSO (Multiwavelength Imaging New Instrument for the EUSO) telescope was built by the JEM-EUSO collaboration. It was brought to the International Space Station (ISS) on 22 August 2019, by the Soyuz MS-14 vehicle and has been operated since then as a part of an agreement between the Italian Space Agency (Agenzia Spaziale Italiana; ASI) and Roscosmos (Russia) [12; 13; 14; 15]. The EUSO-SPB2 stratospheric balloon equipped with a fluorescence and Cherenkov telescopes made a short flight from Wanaka, New Zealand, in May 2023 [16; 17; 18]. All these instruments are aimed to be pathfinders and test beds for full-size orbital experiments like K-EUSO [19] and POEMMA [20]. Similar to the other projects of the JEM-EUSO collaboration, the Mini-EUSO telescope is registering multiple types of UV emission taking place in the nocturnal atmosphere of Earth, among them signals of meteors. A series of studies is dedicated to their search and analysis [21; 22]. In the present paper, we continue our earlier research aimed at developing a method of recognizing meteor tracks in the Mini-EUSO data with neural networks [23]. A motivation for the study is the following. A conventional approach to finding signals of meteors in the Mini-EUSO data is time consuming and prone to numerous false positives. Thus, it is interesting to figure out if an approach based on machine learning (ML) and artificial neural networks (ANNs) can demonstrate higher efficacy than the conventional one so that both approaches complement each other. If so, it is interesting to test if results can be achieved with simple neural networks that can be implemented in the forthcoming orbital experiments, which are unlikely to have powerful onboard processors. These results can also be useful for recognizing tracks of extensive air showers in the future experiments since such signals resemble shapes and kinematics similar to those produced be meteors, though at completely different time scales.