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 high-quality x-rays from XFELs and high-quality ultrafast probes for UED/UEM. 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. Additionally, the application of this work to online digital twins toward higher brightness beams will be discussed.