000006535 001__ 6535
000006535 005__ 20231214170242.0
000006535 037__ $$aCTALK-2023-0124
000006535 100__ $$aGunessee, Akhil
000006535 245__ $$aCan a deep learning approach of detecting solar radio bursts perform better than the interquartile range threshold outlier detection method, currently running on the CALLISTO instrument of the Royal Observatory of Belgium?
000006535 260__ $$c2023
000006535 269__ $$c2023-07-03
000006535 520__ $$aAnyone familiar with the field of solar radio burst detection and classification knows the cumbersome task of having to go through daily observations and manually recording the type of solar radio burst occurring in the spectrograms. Past non-AI based approaches have been applied to avoid or reduce that particular hindrance but to limited success. One of those methods is through the use of an outlier detection rule such as the interquartile range (IQR) threshold method. Briefly, this method registers a burst when the brightness distribution over time of the spectrogram is higher than the sum of the third quartile with the product of 1.5 and its IQR. This simple criterion can be subject to many false positives and false negatives too, where false positives would be mainly due to RFIs. With the recent rise of big data and AI, we present a prototype model of detecting solar radio burst using YOLOv5. The initial prototype was trained on 306 images and resulted in a precision of 59.5% and a recall of 65.9%. This prototype also made us aware that this initial model iteration was susceptible to falsely detecting lightning strikes as bursts. Following these promising results and valuable lessons learnt, another around of annotations was just completed with roughly 1300 new images, and another class being added to the annotated data, that is, lightning strikes. The new annotations will allow the prospective model to not only detect if and how many solar radio bursts are present in a spectrogram but also classify which types are there. Ultimately, being able to correctly differentiate between genuine solar radio bursts and RFIs, and further classifying them into their different solar radio bursts types would be beneficial to monitoring solar activity and useful to the space weather community.
000006535 594__ $$aNO
000006535 6531_ $$aSolar Radio Bursts
000006535 6531_ $$aHumain Radio Astronomy Station
000006535 6531_ $$aCALLISTO
000006535 6531_ $$aInterquartile Range Threshold
000006535 6531_ $$aOutlier Detection
000006535 6531_ $$aDeep Learning
000006535 6531_ $$aObject Detection
000006535 700__ $$aMarqué, Christophe
000006535 700__ $$aMartínez Picar, Antonio
000006535 700__ $$aDolla, Laurent
000006535 700__ $$aDelouille, Veronique
000006535 773__ $$tCESRA, University of Hertfordshire, UK
000006535 8560_ $$fakhil.gunessee@ksb-orb.be
000006535 85642 $$ahttps://star.herts.ac.uk/cesra/
000006535 906__ $$aContributed
000006535 980__ $$aCTALKCONT