Automated detection of solar radio bursts using a statistical method

Abstract

Radio bursts from the solar corona can provide clues to forecast space-weather hazards. After recent technology advancements, regular monitoring of radio bursts has increased and large observational datasets are produced. Hence, manual identification and classification of them is a challenging task. In this article, we describe an algorithm to automatically identify radio bursts from dynamic solar radio spectrograms using a novel statistical method. We use e-CALLISTO (Compound Astronomical Low Cost Low Frequency Instrument for Spectroscopy and Transportable Observatory) radio spectrometer data obtained at Gauribidanur Observatory near Bangalore in India during 2013 – 2014. We have studied the classifier performance using the receiver operating characteristics. Further, we analyze type III bursts observed in the year 2014 and find that 75% of the observed bursts were below 200 MHz. Our analysis shows that the positions of flare sites, which are associated with the type III bursts with upper frequency cutoff 200 MHz originate close to the solar disk center.