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

TL;DR

This paper demonstrates that neural networks can effectively classify data from the TUS orbital telescope, distinguishing cosmic ray signals from lightning flashes with simple models combined with conventional analysis methods.

Contribution

It introduces the application of neural networks to classify TUS telescope data, highlighting their effectiveness in identifying cosmic ray and lightning signals.

Findings

Neural networks can effectively classify TUS data.

Simple neural models combined with conventional methods are highly effective.

Successful differentiation of cosmic ray and lightning signals.

Abstract

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.