Although beam emittance is critical for the performance of high-brightness
accelerators, optimization is often time limited as emittance calculations,
commonly done via quadrupole scans, are typically slow. Such calculations are a
type of multi-point query, i.e. each query requires multiple
secondary measurements. Traditional black-box optimizers such as Bayesian
optimization are slow and inefficient when dealing with such objectives as they
must acquire the full series of measurements, but return only the emittance,
with each query. We propose applying Bayesian Algorithm Execution (BAX) to
instead query and model individual beam-size measurements. BAX avoids the slow
multi-point query on the accelerator by acquiring points through a
virtual objective, i.e. calculating the emittance objective from a
fast learned model rather than directly from the accelerator. Here, we use BAX
to minimize emittance at the Linac Coherent Light Source (LCLS) and the
Facility for Advanced Accelerator Experimental Tests II (FACET-II). In
simulation, BAX is 20× faster and more robust to noise compared to
existing methods. In live LCLS and FACET-II tests, BAX performed the first
automated emittance tuning, matching the hand-tuned emittance at FACET-II and
achieving a 24% lower emittance at LCLS. Our method represents a conceptual
shift for optimizing multi-point queries, and we anticipate that it can be
readily adapted to similar problems in particle accelerators and other
scientific instruments