# cDVAE: Multimodal Generative Conditional Diffusion Guided by Variational Autoencoder Latent Embedding for Virtual 6D Phase Space Diagnostics

arXiv: https://arxiv.org/abs/2407.20218 · HTML: https://mlsanity.com/paper/2407.20218/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2024.

## Built on by (most cited first, 1 of 1)

- [Classifier-pruned Bayesian optimization for particle accelerator tuning: Exploring temporally structured manifold of 6D beam phase space](https://mlsanity.com/paper/2412.01748/) (2024)

## Abstract (arXiv)

Imaging the 6D phase space of a beam in a particle accelerator in a single shot is currently impossible. Single shot beam measurements only exist for certain 2D beam projections and these methods are destructive. A virtual diagnostic that can generate an accurate prediction of a beam's 6D phase space would be incredibly useful for precisely controlling the beam. In this work, a generative conditional diffusion-based approach to creating a virtual diagnostic of all 15 unique 2D projections of a beam's 6D phase space is developed. The diffusion process is guided by a combination of scalar parameters and images that are converted to low-dimensional latent vector representation by a variational autoencoder (VAE). We demonstrate that conditional diffusion guided by VAE (cDVAE) can accurately reconstruct all 15 of the unique 2D projections of a charge particle beam's 6 phase space for the HiRES compact accelerator.