Automated bow shock and magnetopause boundary detection with Cassini using threshold and deep learning methods - Cheng, I Kit; Achilleos, Nicholas; Smith, Andy (Frontiers in Astronomy and Space Sciences, 2022)
Two algorithms set for automatic detection of bow shock (BS) and magnetopause (MP) boundaries at Saturn using in-situ magnetic field and plasma data acquired by the Cassini spacecraft are presented. Traditional threshold-based and modern deep learning algorithms were investigated for the task of boundary detection. The threshold method used two sliding windows with fixed separation over the data to compute parameters to which a threshold was applied to determine the presence of a boundary. The deep learning method used a convolutional neural network (CNN) on images of the electron energy spectrogram to classify the presence of boundaries. 2012 data were held out of the training data to test and compare the algorithms on unseen data. The comparison showed that the CNN method outperformed the threshold method with F1 score of 87.5% for BS crossings and 82.7% for MP crossing on a corrected test dataset. Reliable automated detection of boundary crossings could enable future spacecraft experiments like the PEP instrument on the upcoming JUICE spacecraft mission to dynamically adapt the best observing mode based on rapid classification of the boundary crossings as soon as it appears, thus yielding higher quality data and improved potential for scientific discovery.