Speaker
Description
Digital twins of particle accelerators are increasingly used for experiment planning, machine studies, and model‑based control. Achieving high‑fidelity predictions requires knowledge of machine properties that are difficult to measure directly, such as magnet alignments, transfer function variations, nonlinearities, and stray fields. In this work, we introduce parameterizations to capture these effects and employ Bayesian inference to estimate their values and uncertainties by calibrating a digital twin to orbit response measurements from the AGS Booster at Brookhaven National Laboratory. A machine‑learning emulator trained on a perturbed ensemble of Bmad simulations enables computationally efficient sampling of the high‑dimensional posterior. The resulting joint parameter distribution incorporates BPM uncertainties and provides data‑constrained variations that, when inserted back into the digital twin, significantly improve agreement with measured beam orbits while yielding uncertainty estimates on both parameters and predictions.
Funding Agency
Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704, No. DE-SC0024287, and No. DE-SC0025351 with the U.S. Department of Energy.
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