Advancing accelerator virtual beam diagnostics through latent evolution modeling: an integrated solution to forward, inverse, tuning, and UQ problems

TL;DR

This paper introduces Latent Evolution Model (LEM), a hybrid machine learning framework that uses autoencoders and transformers to improve virtual beam diagnostics by addressing forward, inverse, tuning, and uncertainty quantification problems in accelerator physics.

Contribution

The paper presents a novel integrated framework combining autoencoders and transformers for comprehensive beam diagnostics, enabling efficient forward, inverse, tuning, and uncertainty quantification tasks.

Findings

Successfully encodes 15 phase space projections into a low-dimensional latent space.

Predicts downstream states and upstream phase spaces with uncertainty quantification.

Optimizes RF settings using Bayesian methods within the LEM framework.

Abstract

Virtual beam diagnostics relies on computationally intensive beam dynamics simulations where high-dimensional charged particle beams evolve through the accelerator. We propose Latent Evolution Model (LEM), a hybrid machine learning framework with an autoencoder that projects high-dimensional phase spaces into lower-dimensional representations, coupled with transformers to learn temporal dynamics in the latent space. This approach provides a common foundational framework addressing multiple interconnected challenges in beam diagnostics. For \textit{forward modeling}, a Conditional Variational Autoencoder (CVAE) encodes 15 unique projections of the 6D phase space into a latent representation, while a transformer predicts downstream latent states from upstream inputs. For \textit{inverse problems}, we address two distinct challenges: (a) predicting upstream phase spaces from downstream observations by utilizing the same CVAE architecture with transformers trained on reversed temporal sequences along with aleatoric uncertainty quantification, and (b) estimating RF settings from the latent space of the trained LEM using a dedicated dense neural network that maps latent representations to RF parameters. For \textit{tuning problems}, we leverage the trained LEM and RF estimator within a Bayesian optimization framework to determine optimal RF settings that minimize beam loss. This paper summarizes our recent efforts and demonstrates how this unified approach effectively addresses these traditionally separate challenges.