Implementation of a Near-Realtime Recording and Reporting System of Solar Radio Bursts

TL;DR

This paper presents a near-realtime system for detecting and reporting solar radio bursts, specifically type III bursts, using a deep learning model integrated with the Owens Valley Radio Observatory's Long Wavelength Array.

Contribution

It introduces a low-latency, high-sensitivity radio burst recording system with a YOLO-based deep learning detector trained on synthetic data for real-time space weather monitoring.

Findings

Achieves approximately 10-second reporting latency.

Successfully detects type III radio bursts in real-time.

Demonstrates robustness with synthetic training data.

Abstract

Strong solar activity is often accompanied by a variety of radio bursts. These bursts are valuable diagnostics of coronal and heliospheric processes and also have potential applications in space weather monitoring and forecasting. However, space weather applications require low-latency, high-sensitivity radio burst recording and reporting capabilities, which have remained limited. In this work, we present the development of a near-realtime radio burst recording and reporting system using the Owens Valley Radio Observatory Long Wavelength Array. The system directly clips data from a realtime buffer and streams them as a live radio dynamic spectrogram. These spectrograms are then processed by a deep-learning-based burst identification module for type III radio bursts. The identifier is based on a YOLO (You Only Look Once) architecture and is trained on synthetic type III radio bursts generated using a physics-based model to achieve accurate and robust detection. This system enables continuous realtime radio spectrum streaming and automatic reporting of type III radio bursts within approximately 10 seconds of their occurrence.