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- Title
Image‐Based Classification of Intense Radio Bursts From Spectrograms: An Application to Saturn Kilometric Radiation.
- Authors
O'Dwyer, E. P.; Jackman, C. M.; Domijan, K.; Lamy, L.
- Abstract
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, https://doi.org/10.1002/2017ja024499) and more recently O'Dwyer et al. (2023a, https://doi.org/10.25546/103103) 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. Plain Language Summary: We are using radio observations from the Cassini spacecraft that was in orbit around the planet Saturn for 13 years. We want to search for characteristic features of Saturn's auroral radio emissions (called Saturn Kilometric Radiation or SKR) in the data stream from the radio instrument—specifically events called Low Frequency Extensions (LFEs). The edges of these events can be tracked in time‐frequency spectrograms of Cassini radio observations. We find several hundred examples of the LFEs that we're looking for, and feed these into a computer algorithm which learns what they look like. The algorithm can then be applied to new/unseen data and we allow it to search for similar events. The end result is an extensive catalog of all the LFEs observed throughout the 13‐year near‐Saturn mission by the radio instrument of Cassini. This catalog can be used by the scientific community as a basis for statistical studies of Saturn's radio emissions. The machine learning aspect of this work can be adapted through something known as transfer learning to other planets where we look for similar features in data. Key Points: Supervised learning applied to database of labeled polygons marked on radio spectrogramsFocus on Low Frequency Extensions of Saturn Kilometric Radiation to return a full catalog from the Cassini missionA modified U‐Net architecture achieved median Intersection over Union values of 0.98 and 0.97 across the training and testing set
- Subjects
CASSINI (Spacecraft); SUPERVISED learning; SATURN (Planet); MACHINE learning; PLASMA waves; SPECTROGRAMS; CIRCULAR polarization
- Publication
Journal of Geophysical Research. Space Physics, 2023, Vol 128, Issue 10, p1
- ISSN
2169-9380
- Publication type
Article
- DOI
10.1029/2023JA031926