Detecting the magnetopause of Mercury by neural network — using MESSENGER data to train for BepiColombo.

The BepiColombo mission will arrive at Mercury in 2025. It consists of two spacecraft, which both have a magnetometer on board. One of the science objectives of these instruments is to study the structure of Mercury’s magnetosphere and its dynamical interaction with the solar wind. To study this statistically, a large dataset of observations of the magnetopause (the magnetosphere’s outer boundary) is needed. However, identifying such magnetopause crossings in magnetic field data requires visual inspection by humans with expert knowledge and as such is a very time consuming process. We therefore design an algorithm to automatically detect the Hermean magnetopause in magnetometer time series data, making use of a convolutional neural network.

Since no BepiColombo data (in orbit) is available yet, we train the network on MESSENGER magnetometer data. However, we formulate the problem and design the architecture of the network in such a way that the algorithm should be easily transferable to BepiColombo magnetometer data, avoiding the possible impact of any instrumental particularities or orbital biases.

The goal is to have a neural network which is directly applicable to BepiColombo magnetometer data, as soon as the observations start and without any further training, thereby eliminating the necessity of manually creating a new dataset of BepiColombo magnetopause crossings.