Speaker
Description
Precision control of electron beams is one of the main charges of beam physics, as producing high-brightness beams is critical to numerous accelerator deliverables including AAC applications. Critical to this effort is a set of accurate system models that can inform control policies. To be useful, these models must accurately reflect the behavior of the accelerator. In this work, a systematic, ML-based approach toward this model calibration problem is outlined. We use ML-based, time-efficient approaches, such as multi-fidelity Bayesian optimization, to balance the flow of information from high- and low-fidelity models towards the creation of a calibrated model of the FACET-II injector. Additionally, the application of this work to online digital twins toward higher brightness beams will be discussed.
| Working group | WG5 |
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