Using AI and Machine Learning to reveal the physics of magnetic reconnection, a universal plasma process

In most regions of astrophysical plasma magnetic diffusivity is low and the magnetic field is "frozen in", meaning that different regions of plasma cannot interpenetrate. This leads to the formation of boundary layers, and the storage of magnetic energy. Magnetic reconnection may occur within these boundary layers, known as current sheets, enabling the conversion and release of energy, a changed magnetic topology, and particle acceleration. The central diffusion region of magnetic reconnection plays a crucial role, because this is where the plasma demagnetises. The goal of this project is to take data from Magnetospheric Multiscale (MMS) and other spacecraft missions, and use novel techniques in AI and machine learning to find and analyse MMS observations of reconnection at the fluid level (flow reversals etc.), and to then categorise the underlying plasma distribution functions. This will help explain how complex phase-space structures are caused by reconnection and how they evolve in space and time, and as a function of the boundary conditions. Improved knowledge of the plasma at kinetic scales, represented by the distribution function, will enable a step change in our understanding of how reconnection converts energy.