Improved Type III solar radio burst detection using congruent deep
  learning models
Jeremiah Scully Ronan Flynn Peter Gallagher Eoin Carley Mark Daly
Solar flares are energetic events in the solar atmosphere that are often
linked with solar radio bursts (SRBs). SRBs are observed at metric to
decametric wavelengths and are classified into five spectral classes (Type
I--V) based on their signature in dynamic spectra. The automatic detection and
classification of SRBs is a challenge due to their heterogeneous form.
Near-realtime detection and classification of SRBs has become a necessity in
recent years due to large data rates generated by advanced radio telescopes
such as the LOw Frequency ARray (LOFAR). In this study, we implement congruent
deep learning models to automatically detect and classify Type III SRBs. We
generated simulated Type III SRBs, which were comparable to Type IIIs seen in
real observations, using a deep learning method known as Generative Adversarial
Network (GAN). This simulated data was combined with observations from LOFAR to
produce a training set that was used to train an object detection model known
as YOLOv2 (You Only Look Once). Using this congruent deep learning model
system, we can accurately detect Type III SRBs at a mean Average Precision
(mAP) value of 77.71%.