Generation of Cosmic-Ray Trajectories by a Diffusion Model Trained on Test Particles in 3D Magnetohydrodynamic Turbulence

Abstract Models for the transport of high-energy charged particles through strong magnetic turbulence play a key role in space and astrophysical studies, such as describing the propagation of solar energetic particles and high-energy cosmic rays. Inspired by the recent advances in high-performance machine learning techniques, we investigate the application of generative diffusion models to synthesizing test particle trajectories obtained from a turbulent magnetohydrodynamics simulation. We consider velocity increment, spatial transport, and curvature statistics, and find excellent agreement with the baseline trajectories for fixed particle energies. Additionally, we consider two synthetic turbulence models for comparison. Finally, challenges toward an application-ready transport model based on our approach are discussed.