Evaluation of EAS directions based on TAIGA HiSCORE data using fully connected neural networks
Abstract
The direction of extensive air showers can be used to determine the source of gamma quanta and plays an important role in estimating the energy of the primary particle. The data from an array of non-imaging Cherenkov detector stations HiSCORE in the TAIGA experiment registering the number of photoelectrons and detection time can be used to estimate the shower direction with high accuracy. In this work, we use artificial neural networks trained on Monte Carlo-simulated TAIGA HiSCORE data for gamma quanta to obtain shower direction estimates. The neural networks are multilayer perceptrons with skip connections using partial data from several HiSCORE stations as inputs; composite estimates are derived from multiple individual estimates by the neural networks. We apply a two-stage algorithm in which the direction estimates obtained in the first stage are used to transform the input data and refine the estimates. The mean error of the final estimates is less than 0.25 degrees. The approach will be used for multimodal analysis of the data from several types of detectors used in the TAIGA experiment.
Keywords
Cite
@article{arxiv.2502.13851,
title = {Evaluation of EAS directions based on TAIGA HiSCORE data using fully connected neural networks},
author = {A. P. Kryukov and S. P. Polyakov and Yu. Yu. Dubenskaya and E. O. Gres and E. B. Postnikov and P. A. Volchugov and D. P. Zhurov},
journal= {arXiv preprint arXiv:2502.13851},
year = {2025}
}
Comments
The work was reported on the 8th International Conference on Deep Learning in Computational Physics (DLCP2025), June 19-21, 2024, Moscow, Russia (https://dlcp2024.sinp.msu.ru/). To bee published in Moscow University Physics Bulletin