Using supervised machine learning to automatically detect type II and III solar radio bursts

Solar flares are often associated with high-intensity radio emission known as `solar radio bursts' (SRBs). SRBs are generally observed in dynamic spectra and have five major spectral classes, labelled type I to type V depending on their shape and extent in frequency and time. Due to their morphological complexity, a challenge in solar radio physics is the automatic detection and classification of such radio bursts. Classification of SRBs has become necessary in recent years due to large data rates (3 Gb/s) generated by advanced radio telescopes such as the Low Frequency Array (LOFAR). Here we test the ability of several supervised machine learning algorithms to automatically classify type II and type III solar radio bursts. We test the detection accuracy of support vector machines (SVM), random forest (RF), as well as an implementation of transfer learning of the Inception and YOLO convolutional neural networks (CNNs). The training data was assembled from type II and III bursts observed by the Radio Solar Telescope Network (RSTN) from 1996 to 2018, supplemented by type II and III radio burst simulations. The CNNs were the best performers, often exceeding >90% accuracy on the validation set, with YOLO having the ability to perform radio burst burst localisation in dynamic spectra. This shows that machine learning algorithms (in particular CNNs) are capable of SRB classification, and we conclude by discussing future plans for the implementation of a CNN in the LOFAR for Space Weather (LOFAR4SW) data-stream pipelines.