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

2026-02-25

AI Summary

This work addresses the coupled challenges in virtual diagnostics of high-dimensional charged particle beams in accelerators—namely forward modeling, inverse problem solving, beam tuning optimization, and uncertainty quantification—by proposing a Latent-space Evolution Model (LEM). LEM uniquely integrates a variational autoencoder with a time-series Transformer to construct a unified framework within a low-dimensional latent space, enabling simultaneous handling of these four core tasks. By combining a conditional variational autoencoder, dense neural networks, and Bayesian optimization, the method efficiently infers upstream phase-space distributions from downstream observations, accurately estimates radiofrequency parameters, and significantly reduces beam loss. This approach establishes a new paradigm for intelligent accelerator diagnostics through end-to-end learning in a compact latent representation.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Learning to Search
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• virtual beam diagnostics
• forward modeling
• inverse problems
• beam tuning
• uncertainty quantification

Innovation

• Latent Evolution Model
• Virtual Beam Diagnostics
• Transformer-based Dynamics
• Inverse Problem with UQ
• Bayesian Optimization for Tuning