Application of neural networks to classification of data of the TUS orbital telescope
This work addresses data classification challenges for the TUS telescope, an orbital detector of ultra-high energy cosmic rays, but it is incremental as it applies existing methods to new data.
The authors tackled the problem of classifying signals from the TUS orbital telescope, specifically distinguishing cosmic ray hits from distant lightning flashes, and demonstrated that simple neural networks combined with conventional methods are highly effective for this task.
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.