Prior-mean-assisted Bayesian optimization application on FRIB Front-End tunning

The Facility for Rare Isotope Beams (FRIB) at Michigan State University (MSU) is designed for various kinds of rare isotope production. This involves the frequent switch of the ion source species. Therefore, fast tuning of the accelerator Front-End (FE) to maintain optimal beam optics is one of the key performance requirements. Breaking-through the tuning performance over the traditional black-box optimization algorithm may be possible if historical or simulated data can be incorporated into the optimization algorithm in a computationally feasible way. However, we experienced significant machine status changes (a.k.a. 'distribution shift' or'machine drift') whenever ion source species are switched or the ion source is re-started (after overnight turn-off).