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 recent years, neural networks of various configurations have been increasingly used to analyze data obtained with fluorescent and Cherenkov telescopes. In particular, a whole series of studies dedicated to the analysis of gamma-ray astronomy data with neural networks has been performed by the VERITAS [1], TAIGA [2, 3], and CTA [4, 5] collaborations. Typical tasks are the recognition of particular signal patterns in the data flow. In the simplest case, the problem can be reduced to classifying data into two groups: data samples that contain a signal of the desired type and all the rest. Since data obtained with the help of telescopes can naturally be considered as images or animations, one of the popular tools for classifying them are convolutional neural networks (CNNs), created primarily for image classification. CNNs have demonstrated the highest efficiency in this class of problems, see, for example, [6, 7].