Type III solar radio burst detection and classification: A deep learning approach

TL;DR

This paper introduces a deep learning approach using YOLOv2 for real-time detection and classification of Type III solar radio bursts, improving accuracy and efficiency over traditional methods.

Contribution

The study presents the first application of YOLOv2 deep learning model for automatic, real-time classification of Type III solar radio bursts.

Findings

Achieves higher accuracy than traditional shape-based algorithms.

Enables real-time classification suitable for large radio telescope data.

Demonstrates effectiveness on simulated and real solar radio burst data.

Abstract

Solar Radio Bursts (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 complex characterisation, a challenge in solar radio physics is the automatic detection and classification of such radio bursts. Classification of SRBs has become fundamental in recent years due to large data rates generated by advanced radio telescopes such as the LOw-Frequency ARray, (LOFAR). Current state-of-the-art algorithms implement the Hough or Radon transform as a means of detecting predefined parametric shapes in images. These algorithms achieve up to 84% accuracy, depending on the Type of radio burst being classified. Other techniques include procedures that rely on Constant-FalseAlarm-Rate detection, which is essentially detection of radio bursts using a de-noising and adaptive threshold in dynamic spectra. It works well for a variety of different Types of radio bursts and achieves an accuracy of up to 70%. In this research, we are introducing a methodology named You Only Look Once v2 (YOLOv2) for solar radio burst classification. By using Type III simulation methods we can train the algorithm to classify real Type III solar radio bursts in real-time at an accu