Generation of cosmic ray trajectories by a Diffusion Model trained on test particles in 3D magnetohydrodynamic turbulence

TL;DR

This paper introduces a diffusion model trained on test particle trajectories in 3D magnetohydrodynamic turbulence, demonstrating high accuracy in reproducing particle transport statistics relevant to space physics.

Contribution

It presents a novel application of generative diffusion models to simulate cosmic ray trajectories in turbulent magnetic fields, outperforming traditional models in key statistical measures.

Findings

Excellent agreement with baseline trajectories for fixed energies

Effective reproduction of velocity, spatial, and curvature statistics

Comparison with synthetic turbulence models shows robustness

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 towards an application-ready transport model based on our approach are discussed.