Image-Based Classification of Intense Radio Bursts From Spectrograms: An Application to Saturn Kilometric Radiation

Saturn Kilometric Radiation (SKR) is a non-thermal auroral emission with peak emission occurring at 100-400 kHz. Its properties have been extensively studied since Cassini's arrival at Saturn until mission end with its Radio and Plasma Wave Science (RPWS) experiment. Low Frequency Extensions (LFEs) of SKR which consist of global intensifications of SKR accompanied by extensions of the main SKR band down to lower frequencies have been studied in particular. Low Frequency Extensions result from internally driven tail reconnection and from solar wind compressions of the magnetosphere, which also trigger tail reconnection. They have been cataloged through visual inspection with two approaches, using an intensity threshold for LFEs in 2006 (Reed et al., 2018, ) and more recently O'Dwyer et al. (2023a, ) produced a sample of LFEs detected by Cassini/RPWS by fitting their exact frequency-time coordinates with polygons. In this study we use the latter catalog of LFEs as a training set for an image based machine learning algorithm to classify all LFEs detected by Cassini/RPWS. The inputs to the model are multi-channel images consisting of spectrogram images in flux density and degree of circular polarization. The outputs of the model are binary masks showing the exact location of the LFE in frequency-time space. The median Intersection Over Union across the testing and training set were calculated to be 0.97 and 0.98, respectively. The output of this study is a list of all 4,874 LFEs detected using this method. The list of LFE frequency-time coordinates is available for use amongst the scientific community.