Authors

Abstract

Particle accelerators are invaluable discovery engines in the chemical, biological and physicalsciences. Characterization of the accelerated beam response to accelerator input parametersis often the first step when conducting accelerator-based experiments. Currently usedtechniques for characterization, such as grid-like parameter sampling scans, becomeimpractical when extended to higher dimensional input spaces, when complicated measurement constraints are present, or prior information known about the beam response isscarce. Here in this work, we describe an adaptation of the popular Bayesian optimizationalgorithm, which enables a turn-key exploration of input parameter spaces. Our algorithmreplaces the need for parameter scans while minimizing prior information needed about themeasurements behavior and associated measurement constraints. We experimentallydemonstrate that our algorithm autonomously conducts an adaptive, multi-parameterexploration of input parameter space, potentially orders of magnitude faster than conventionalgrid-like parameter scans, while making highly constrained, single-shot beam phasespacemeasurements and accounts for costs associated with changing input parameters. Inaddition to applications in accelerator-based scientific experiments, this algorithm addresseschallenges shared by many scientific disciplines, and is thus applicable to autonomouslyconducting experiments over a broad range of research topics.