Speaker
Description
Solar atmospheric plasmas host highly dynamical magnetohydrodynamic (MHD) processes, including reconnection, shocks, turbulence, and waves, that couple the photosphere, chromosphere, and corona and ultimately seed variability throughout the heliosphere. This talk connects these drivers to their signatures in the solar wind and at Earth’s ionosphere, emphasizing the need for consistent “Sun-to-geospace” diagnostics. I will highlight how modern collaborative efforts are tackling long-standing obstacles in identifying and quantifying MHD wave modes in structured magnetic features, including the role of radiative transfer, partial ionisation, and multi-height spectropolarimetry. I will then demonstrate how modern machine learning is accelerating plasma inference through use of neural classifiers for Stokes-profile morphologies, transformer-based stratified inversions, and the emerging promise of Physics-Informed Neural Networks (PINNs) to connect inversions directly to governing MHD physics. Remaining challenges include the domain shift between simulations and observations, and the robust coupling of wave energetics to atmospheric heating. Finally, I will outline how next-generation instrumentation, such as integral field spectropolarimeters and next-generation space missions such as the Galileo Solar Space Telescope, will deliver the cadence, sensitivity, and coverage needed to close these gaps.