Evaluation of EAS directions based on TAIGA HiSCORE data using fully connected neural networks
Journal:
arXiv
Published Date:
Feb 19, 2025
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.
