Deep Active Learning with Artificial Neural Networks for Automatic Detection of Mercury’s Bow Shock and Magnetopause Crossing Signatures in NASA’s MESSENGER Magnetometer Observations
2021 · Active learning for detecting Mercury's bow shock and magnetopause crossings.
Between 2011 and 2015, NASA ’s MESSENGER spacecraft orbited Mercury, where it collected magnetic measurements through the onboard magnetometer. With these, several studies attempted to geometrically model the exact shape of the planet’s bow shock and magnetopause boundaries. However, despite being highly complex, these static models struggle to adapt to changing environments. In cases like this, where it is necessary to capture fine structures in discrete signals, deep learning has been able to outperform traditional modeling in various applications over the last decade. Hence, we devise a deep neural network that identifies the spacecraft’s bow shock and magnetopause crossings within a local time window of measurements and predicts upcoming crossings beyond that window. The model achieves an overall macro F1 of 0.82 and accuracies of 80 % and 88 % on the bow shock and magnetopause crossings, respectively. Furthermore, we employ an active learning paradigm to determine how many Mercury years’ worth of observations are required for a representative model. We find that two Mercury years’ worth of measurements are sufficient for a satisfactory performance, which is only about a tenth of the entire data. This work may be relevant to future research concerning the BepiColombo mission by ESA and JAXA, whose space probes will enter orbit around the planet in December 2025.