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

• URL: http://arxiv.org/abs/2501.04305v2
• Date: Sat, 11 Jan 2025 23:00:58 GMT
• Title: Physics-Informed Super-Resolution Diffusion for 6D Phase Space Diagnostics
• Authors: Alexander Scheinker,
• Abstract summary: An adaptive variational autoencoder embeds initial beam condition images and scalar measurements to a low-dimensional latent space. Projecting from a 6D tensor generates physically consistent 2D projections. Un-supervised adaptive latent space tuning enables tracking of time-varying beams.
• Score: 55.2480439325792
• License:
• 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.

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