A Novel Method to Train Classification Models for Structure Detection in In-situ Spacecraft Data

We present a method for creating spacecraft-like data which can be used to train Machine Learning (ML) models to detect and classify structures in in-situ spacecraft data. First, we use the Grad-Shafranov (GS) equation to numerically solve for several magnetohydrostatic equilibria which are variations on a known analytic equilibrium. These equilibria are then used as the initial conditions for Particle-In-Cell (PIC) simulations in which the structures of interest are observed and labeled. We then take one-dimensional slices through the simulations to replicate what a spacecraft collecting data from the simulation would observe. This sliced data then can be used as training data for ML structure detection models. We demonstrate the method applied to the detection of small-scale plasmoids, which are important to understanding magnetotail reconnection dynamics. The simple 1D classifier we train is able to detect 77% of the plasmoid points in the dataset but also produces many false positives. Our further work on this problem is detailed, and additional uses of the method are discussed.