Physics-constrained superresolution diffusion for six-dimensional phase space diagnostics

Adaptive physics-constrained superresolution diffusion is developed for noninvasive virtual diagnostics of the six-dimensional (6D) phase space density of charged particle beams. An adaptive variational autoencoder embeds initial beam condition images and scalar measurements to a low-dimensional latent space from which a <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:msup><a:mn>32</a:mn><a:mn>6</a:mn></a:msup></a:math> pixel 6D tensor representation of the beam's 6D phase space density is generated. Projecting from a 6D tensor generates physically consistent two-dimensional projections. Physics-guided superresolution diffusion transforms low-resolution images of the 6D density to high resolution <b:math xmlns:b="http://www.w3.org/1998/Math/MathML"><b:mrow><b:mn>256</b:mn><b:mo>×</b:mo><b:mn>256</b:mn></b:mrow></b:math> pixel images. Unsupervised 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 multiparticle 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 retraining.