Physics-Informed Super-Resolution Diffusion for 6D Phase Space Diagnostics

TL;DR

This paper introduces a physics-informed super-resolution diffusion method using a variational autoencoder to non-invasively reconstruct high-resolution 6D phase space densities of charged particle beams, enabling real-time diagnostics and robust tracking.

Contribution

It presents a novel adaptive variational autoencoder framework that combines physics-guided super-resolution diffusion with unsupervised latent space tuning for dynamic beam diagnostics.

Findings

Successfully reconstructs high-resolution 6D phase space from low-res images.

Demonstrates robustness to distribution shifts without re-training.

Validates approach with experimental data and simulations.

Abstract

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.