Automatic detection of solar radio bursts in NenuFAR observations
Item Type:Conference Paper
Citation:Murphy, P. C., Cecconi, B., Briand, C., Aicardi, S., Automatic detection of solar radio bursts in NenuFAR observations, In C. K. Louis, C. M. Jackman, G. Fischer, A. H. Sulaiman, P. Zucca, Dublin Institute for Advanced Studies (Eds.), Planetary, Solar and Heliospheric Radio Emissions IX, 2023. https://doi.org/10.25546/104055
Murphy_2023_PRE9_accepted.pdf (Accepted for publication (author's copy) - Peer Reviewed) 1.613Mb
Solar radio bursts are some of the brightest emissions at radio frequencies in the solar system. The emission mechanisms that generate these bursts offer a remote insight into physical processes in solar coronal plasma, while fine spectral features hint at its underlying turbulent nature. During radio noise storms many hundreds of solar radio bursts can occur over the course of a few hours. Identifying and classifying solar radio bursts is often done manually although a number of automatic algorithms have been produced for this purpose. The use of machine learning algorithms for image segmentation and classification is well established and has shown promising results in the case of identifying Type II and Type III solar radio bursts. Here we present the results of a convolutional neural network applied to dynamic spectra of NenuFAR solar observations. We highlight some initial success in segmenting radio bursts from the background spectra and outline the steps necessary for burst classification.
Type of material:Conference Paper
Availability:Full text available