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

In recent years, neural networks of various configurations have been increasingly used to analyze data obtained with fluorescent and Cherenkov telescopes. In particular, a whole series of studies dedicated to the analysis of gamma-ray astronomy data with neural networks has been performed by the VERITAS [1], TAIGA [2, 3], and CTA [4, 5] collaborations. Typical tasks are the recognition of particular signal patterns in the data flow. In the simplest case, the problem can be reduced to classifying data into two groups: data samples that contain a signal of the desired type and all the rest. Since data obtained with the help of telescopes can naturally be considered as images or animations, one of the popular tools for classifying them are convolutional neural networks (CNNs), created primarily for image classification. CNNs have demonstrated the highest efficiency in this class of problems, see, for example, [6, 7].