Application of Convolutional Neural Networks for Data Analysis in TAIGA-HiSCORE Experiment

The Tunka Advanced Instrument for gamma-ray and cosmic ray Astrophysics (TAIGA) is a hybrid observatory for the detection of extensive air showers (EAS), produced by high-energy gamma rays and cosmic rays. The complex consists of such facilities as TAIGA-IACT, TAIGA-HiSCORE, and a variety of others. The goal of the study is to introduce a deep learning-based technique for EAS axis reconstruction. A convolutional neural network (CNN) model is proposed, while HiSCORE events, consisting of time-amplitude data, are treated as images by the model. Reasoning behind the CNN model and model efficacy will be discussed, along with [preliminary] results for EAS axis direction determination. This article will show that the accuracy of the model reaches 1 degrees-2 degrees for the zenith and azimuthal angles, however, the accuracy of the model does not reach the accuracy of conventional methods.