Automated Detection of Solar Radio Bursts Using a Statistical Method
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 \(\gtrsim200\) MHz originate close to the solar disk center.
KeywordsCorona, radio emission Radio bursts Instrumentation and data management
D.S. acknowledges the INSPIRE-SHE program of the Department of Science and Technology, India. K.S.R. acknowledges the financial support from the Science and Engineering Research Board (SERB), Department of Science and Technology, India (PDF/2015/000393). K.S.R. acknowledges the NVIDIA Corporation for supporting this project by donating the Titan Xp GPU. The authors would like to thank the anonymous referee for his/her comments that helped in improving the manuscript.
Disclosure of Potential Conflicts of Interest
The author declares that there are no conflicts of interest.
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