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

2025-01-08

AI Summary

Non-invasive, high-precision detection and robust tracking of charged particle beams in 6D phase space remain challenging due to limited resolution and time-varying dynamics. Method: We propose the first adaptive, physics-guided super-resolution diffusion framework, integrating an adaptive variational autoencoder (VAE) with a physically constrained diffusion model. The method represents phase-space density as a 6D tensor and enables online latent-space adaptation without time-dependent initial-prior assumptions. Contribution/Results: Our approach reconstructs high-resolution (256×256) 6D phase-space densities from low-resolution measurements, enabling unsupervised, time-resolved beam tracking and physically consistent 2D projection generation. It exhibits robustness to distribution shift and eliminates the need for retraining. Validated on the HiRES ultrafast electron diffraction (UED) experiment, the framework establishes a generalizable virtual diagnostics paradigm for complex, high-dimensional dynamical systems.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Non-contact Detection
• 6D Phase Space
• Charged Particle Tracking

Innovation

• Physics-Informed Enhancements
• Non-Contact Charged Particle Imaging
• Stable Tracking in 6D Phase Space