  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.

Instrumentation and Methods for Astrophysics;Machine Learning