# A Neural Network Approach for Selecting Track-like Events in Fluorescence Telescope Data

arXiv: https://arxiv.org/abs/2212.03787 · HTML: https://mlsanity.com/paper/2212.03787/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 7 Dec 2022.

## Builds on (most cited first, 2 of 2)

- [Application of neural networks to classification of data of the TUS orbital telescope](https://mlsanity.com/paper/2106.03361/) (2021)
- [Processing Images from Multiple IACTs in the TAIGA Experiment with Convolutional Neural Networks](https://mlsanity.com/paper/2112.15382/) (2021)

## Built on by (most cited first, 1 of 1)

- [Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data](https://mlsanity.com/paper/2311.14983/) (2023)

## Abstract (arXiv)

In 2016-2017, TUS, the world's first experiment for testing the possibility of registering ultra-high energy cosmic rays (UHECRs) by their fluorescent radiation in the night atmosphere of Earth was carried out. Since 2019, the Russian-Italian fluorescence telescope (FT) Mini-EUSO ("UV Atmosphere") has been operating on the ISS. The stratospheric experiment EUSO-SPB2, which will employ an FT for registering UHECRs, is planned for 2023. We show how a simple convolutional neural network can be effectively used to find track-like events in the variety of data obtained with such instruments.