Modeling Images of Proton Events for the TAIGA Project Using a Generative Adversaria Network: Features of the Network Architecture and the Learning Process

Abstract

High-energy particles interacting with the Earth atmosphere give rise to extensive air showers emitting Cherenkov light. This light can be detected on the ground by imaging atmospheric Cherenkov telescopes (IACTs). One of the main problems solved during primary processing of experimental data is the separation of signal events (gamma quanta) against the hadronic background, the bulk of which is made up of proton events. To ensure correct gamma event/proton event separation under real conditions, a large amount of experimental data, including model data, is required. Thus, although proton events are considered as background, their images are also necessary for accurate registration of gamma quanta. We applied a machine learning method, namely the generative adversarial networks (GANs) to generate images of proton events for the TAIGA project. This approach allowed us to significantly increase the speed of image generation. At the same time testing the results using third-party software showed that over 95% of the generated images are correct and can be used in the experiment. In this article we provide a detailed GAN architecture suitable for generating images of proton events similar to those obtained from IACTs of the TAIGA project. The features of the training process are also discussed, including the number of learning epochs and selecting appropriate network parameters.