# Application of neural networks to classification of data of the TUS orbital telescope

arXiv: https://arxiv.org/abs/2106.03361 · HTML: https://mlsanity.com/paper/2106.03361/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 7 Jun 2021.

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

- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [Machine Learning Approach for Air Shower Recognition in EUSO-SPB Data](https://mlsanity.com/paper/1909.03680/) (2019)
- [Deep learning with photosensor timing information as a background rejection method for the Cherenkov Telescope Array](https://mlsanity.com/paper/2103.06054/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Neural Network Approach for Selecting Track-like Events in Fluorescence Telescope Data](https://mlsanity.com/paper/2212.03787/) (2022)
- [Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data](https://mlsanity.com/paper/2311.14983/) (2023)

## Abstract (arXiv)

We employ neural networks for classification of data of the TUS fluorescence telescope, the world's first orbital detector of ultra-high energy cosmic rays. We focus on two particular types of signals in the TUS data: track-like flashes produced by cosmic ray hits of the photodetector and flashes that originated from distant lightnings. We demonstrate that even simple neural networks combined with certain conventional methods of data analysis can be highly effective in tasks of classification of data of fluorescence telescopes.