Simulating Solar Radio Bursts Using Generative Adversarial Networks

Abstract

Solar flares are one of the most extreme drivers of space weather in our solar system. The impulsive solar radio emission associated with a solar flare is known as a solar radio burst (SRB). They are generally studied in dynamic spectra and are classified into five major spectral classes, ranging from Type I to Type V, based on their form and frequency, and time duration. Due to their intricate characterisation, generating a training set for object-detection and classification models of such phenomena is a difficulty in machine learning. Current algorithms implement parametric modelling where the quantity, grouping, intensity, drift rate, heterogeneity, start–end frequency and start–end time of Type-III and Type-II radio bursts are all random. However, this model does not factor in the true shape or general features seen in real dynamic spectra observations of the Sun, which can be crucial when training classification or object-detection algorithms. In this research, we introduce a methodology named a Generative Adversarial Network (GAN) for generating realistic SRB simulations. By using real examples of Type-III and Type-II SRB data, we can train GANs to generate images almost comparable to real observed data. Furthermore, we evaluate the results of the generated model using human perception, then we compare and contrast the results using a metric known as the Fréchet Inception Distance.