Speaker
Description
Digital twins of particle accelerators are used to plan and control operations and to design data collection campaigns. Accurate modeling typically requires knowledge of quantities that are hard to measure directly, e.g., magnet alignments, power supply transfer functions, magnet nonlinearities, and stray fields. In this work we introduce parameters to describe these effects and use Bayesian methods to probabilistically estimate their values and uncertainties by calibrating digital twins to beam measurements performed at the AGS and its Booster at Brookhaven National Laboratory. The inference is computationally accelerated using a machine learning emulator of the physical accelerator digital twin trained to a perturbed-parameter ensemble of Bmad simulations. The result is a joint posterior distribution over the parameters constrained by the data and taking into account beam monitor errors. Incorporating estimates of these parameters into the digital twin is shown to result in a significant improvement in the quality of the model and provides error bars on the model parameters and predictions.
Funding Agency
Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy and by DOE-NP No. DE SC-0024287.
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