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

arXiv: https://arxiv.org/abs/2311.14983 · HTML: https://mlsanity.com/paper/2311.14983/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 25 Nov 2023.

## Builds on (most cited first, 3 of 3)

- [Machine Learning Approach for Air Shower Recognition in EUSO-SPB Data](https://mlsanity.com/paper/1909.03680/) (2019)
- [Application of neural networks to classification of data of the TUS orbital telescope](https://mlsanity.com/paper/2106.03361/) (2021)
- [A Neural Network Approach for Selecting Track-like Events in Fluorescence Telescope Data](https://mlsanity.com/paper/2212.03787/) (2022)

## Abstract (arXiv)

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.