Connecting hybrid plasma simulations of collisionless shockwaves to in situ observations with machine learning

Both observations and simulations have revealed that magnetic reconnection occurs at thin current sheets within the transition region of collisionless shock waves. These ion- and electron-scale structures arise from stream instabilities and turbulence in the shock layer, contribute significantly to repartition of energy across the shock, and propagate far into the downstream region. In a recent study [Gingell et al. 2023, Physics of Plasmas, 30, 0123902], a series of 2D hybrid particle-in-cell simulations were used to explore the shock-driven generation and decay of reconnecting structures over a broad range of parameters. Magnetic field line integration was used to quantify reconnection in each simulation, classifying each cell in the domain as having “closed” or “open” magnetic field topology. Here, we use these classifications to train a convolution neural network (CNN) to identify regions of the simulation that are undergoing (or have undergone) magnetic reconnection. This is performed by splitting each simulation domain into a series of 1D virtual trajectories, with a view to creating a dataset equivalent to a series of in situ observations. We find that the trained CNN is able to effectively identify structures of interest in simulations that have different plasma and shock parameters to the training data set, as well as in those with different dimensionality (i.e. 3D simulations). Further, we present a pipeline for applying this simulation-trained CNN to in situ observations of shocks by the Magnetospheric Multiscale and Solar Orbiter spacecraft, and demonstrate successful detection of reconnection sites embedded in the shock layer. We discuss these techniques more generally as a case study for using machine learning to identify structures of interest in spacecraft data, which may contribute to on-board event selection for burst modes in spacecraft with relatively limited downlink capacity.