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| TUPA34 |
Model-Based Calibration of Control Parameters at the Argonne Wakefield Accelerator |
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- I.P. Sugrue, B. Mustapha, P. Piot, J.G. Power
ANL, Lemont, Illinois, USA
- N. Krislock
Northern Illinois University, DeKalb, Illinois, USA
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Particle accelerators utilize a large number of control parameters to generate and manipulate beams. Digital models and simulations are often used to find the best operating parameters to achieve a set of given beam parameters. Unfortunately, the optimized physics parameters cannot precisely be set in the control system due to, e.g., calibration uncertainties. We developed a data-driven physics-informed surrogate model using neural networks to replace digital models relying on beam-dynamics simulations. This surrogate model can then be used to perform quick diagnostics of the Argonne Wakefield accelerator in real time using nonlinear least-squares methods to find the most likely operating parameters given a measured beam distribution.
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| DOI • |
reference for this paper
※ doi:10.18429/JACoW-NAPAC2022-TUPA34
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| About • |
Received ※ 05 August 2022 — Revised ※ 09 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 24 September 2022 |
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