Physics-Informed Super-Resolution Diffusion for 6D Phase Space Diagnostics
This addresses the challenge of tracking time-varying beams in complex dynamic systems like particle accelerators, though it appears incremental as it builds on existing diffusion and VAE techniques for a specific domain.
The paper tackles the problem of non-invasive virtual diagnostics for the 6D phase space density of charged particle beams by developing an adaptive physics-informed super-resolution diffusion method, resulting in the generation of high-resolution 256x256 pixel images from low-resolution inputs and demonstrating robustness to distribution shift without re-training.
Adaptive physics-informed super-resolution diffusion is developed for non-invasive virtual diagnostics of the 6D phase space density of charged particle beams. An adaptive variational autoencoder (VAE) embeds initial beam condition images and scalar measurements to a low-dimensional latent space from which a 326 pixel 6D tensor representation of the beam's 6D phase space density is generated. Projecting from a 6D tensor generates physically consistent 2D projections. Physics-guided super-resolution diffusion transforms low-resolution images of the 6D density to high resolution 256x256 pixel images. Un-supervised adaptive latent space tuning enables tracking of time-varying beams without knowledge of time-varying initial conditions. The method is demonstrated with experimental data and multi-particle simulations at the HiRES UED. The general approach is applicable to a wide range of complex dynamic systems evolving in high-dimensional phase space. The method is shown to be robust to distribution shift without re-training.