Generation of Grid Surface Detector Data in the Telescope Array Experiment Using Neural Networks

Abstract

In this article, we talk about generating data obtained in the Telescope Array experiment. For this we are using Wasserstein’s generative adversarial networks. Wasserstein’s generative adversarial networks were trained on data obtained using the Monte Carlo method. To improve the quality of the generation, we add the loss function for the generator, which is based on the physics of the process of spreading an extensive air shower. In the future, this network can be used to search for anomalies and for faster data generation, compared with algorithms based on the Monte Carlo method.