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[2602.22618] Advancing accelerator virtual beam diagnostics through latent evolution modeling: an integrated solution to forward, inverse, tuning, and UQ problems
Summary
This article presents a novel hybrid machine learning framework, Latent Evolution Model (LEM), for advancing virtual beam diagnostics in accelerator physics, addressing various challenges in beam dynamics simulations.
Why It Matters
The integration of machine learning in accelerator physics can significantly enhance the accuracy and efficiency of beam diagnostics. This research provides a comprehensive framework that not only improves forward and inverse modeling but also optimizes tuning processes, which is crucial for advancing accelerator technology and enhancing experimental outcomes.
Key Takeaways
- The Latent Evolution Model (LEM) combines autoencoders and transformers for effective beam diagnostics.
- LEM addresses forward modeling, inverse problems, and tuning challenges in accelerator physics.
- The framework utilizes Bayesian optimization to minimize beam loss through optimal RF settings.
Physics > Accelerator Physics arXiv:2602.22618 (physics) [Submitted on 26 Feb 2026] Title:Advancing accelerator virtual beam diagnostics through latent evolution modeling: an integrated solution to forward, inverse, tuning, and UQ problems Authors:Mahindra Rautela, Alexander Scheinker View a PDF of the paper titled Advancing accelerator virtual beam diagnostics through latent evolution modeling: an integrated solution to forward, inverse, tuning, and UQ problems, by Mahindra Rautela and Alexander Scheinker View PDF HTML (experimental) 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 architectur...
