Type II Solar Radio Burst Segmentation and Detection using Multi-Model Deep Learning Networks

Type II Solar Radio Bursts (SRBs) are the result of particle acceleration by shock waves in the solar corona and interplanetary medium. The shocks are created by solar eruptions involving coronal mass ejections traveling at super-alfvenic speeds. The automatic detection, classification, and segmentation of such radio bursts is a challenge in solar radio physics due to their heterogeneous form. Large data rates produced by cutting-edge radio telescopes like the LOw-Frequency ARray (LOFAR) have made SRB detection and classification more feasible in recent years. In this study, we use a Generative Adversarial Network (GAN) to simulate Type II SRBs and then use this simulated data as a training set for a Mask R-CNN to detect and segment Type II SRBs automatically. Using this multi-model approach, we can accurately detect and segment Type II SRBs with a mean Average Precision (mAP) score of 78.90%.