Deep Active Learning for Detection of Mercury’s Bow Shock and Magnetopause Crossings

Accurate and timely detection of bow shock and magnetopause crossings is essential for understanding the dynamics of a planet’s magnetosphere. However, for Mercury, due to the variable nature of its magnetosphere, this remains a challenging task. Existing approaches based on geometric equations only provide average boundary shapes, and can be hard to generalise to environments with variable conditions. On the other hand, data-driven methods require large amounts of annotated data to account for variations, which can scale up the costs quickly. We propose to solve this problem with machine learning. To this end, we introduce a suitable dataset, prepared by processing raw measurements from NASA’s MESSENGER (MErcury Surface, Space Environment, GEochemistry, and Ranging) mission and design a five-class supervised learning problem. We perform an architectural search to find a suitable model, and report our best model, a Convolutional Recurrent Neural Network (CRNN), achieves a macro F1 score of 0.82 with accuracies of approximately 80% and 88% on the bow shock and magnetopause crossings, respectively. Further, we introduce an approach based on active learning that includes only the most informative orbits from the MESSENGER dataset measured by Shannon entropy. We observe that by employing this technique, the model is able to obtain near maximal information gain by training on just two Mercury years worth of data, which is about 10% of the entire dataset. This has the potential to significantly reduce the need for manual labeling. This work sets the ground for future machine learning endeavors in this direction and may be highly relevant to future missions such as BepiColombo, which is expected to enter orbit around Mercury in December 2025.