Evaluation of EAS directions based on TAIGA HiSCORE data using fully connected neural networks

TL;DR

This paper demonstrates that neural networks trained on simulated TAIGA HiSCORE data can estimate the direction of extensive air showers with high accuracy, aiding gamma-ray source identification.

Contribution

It introduces a neural network-based method for precise shower direction estimation using HiSCORE data, including a two-stage refinement process.

Findings

Mean error of direction estimates is less than 0.25 degrees

Neural networks effectively utilize multi-station data for accurate predictions

Two-stage algorithm improves estimation accuracy

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.