Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning

Ryan Roussel (), Juan Pablo Gonzalez-Aguilera, Young-Kee Kim, Eric Wisniewski, Wanming Liu, Philippe Piot, John Power, Adi Hanuka and Auralee Edelen
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Ryan Roussel: University of Chicago
Juan Pablo Gonzalez-Aguilera: University of Chicago
Young-Kee Kim: University of Chicago
Eric Wisniewski: Argonne National Laboratory
Wanming Liu: Argonne National Laboratory
Philippe Piot: Argonne National Laboratory
John Power: Argonne National Laboratory
Adi Hanuka: SLAC National Laboratory
Auralee Edelen: SLAC National Laboratory

Nature Communications, 2021, vol. 12, issue 1, 1-7

Abstract: Abstract Particle accelerators are invaluable discovery engines in the chemical, biological and physical sciences. Characterization of the accelerated beam response to accelerator input parameters is often the first step when conducting accelerator-based experiments. Currently used techniques for characterization, such as grid-like parameter sampling scans, become impractical when extended to higher dimensional input spaces, when complicated measurement constraints are present, or prior information known about the beam response is scarce. Here in this work, we describe an adaptation of the popular Bayesian optimization algorithm, which enables a turn-key exploration of input parameter spaces. Our algorithm replaces the need for parameter scans while minimizing prior information needed about the measurement’s behavior and associated measurement constraints. We experimentally demonstrate that our algorithm autonomously conducts an adaptive, multi-parameter exploration of input parameter space, potentially orders of magnitude faster than conventional grid-like parameter scans, while making highly constrained, single-shot beam phase-space measurements and accounts for costs associated with changing input parameters. In addition to applications in accelerator-based scientific experiments, this algorithm addresses challenges shared by many scientific disciplines, and is thus applicable to autonomously conducting experiments over a broad range of research topics.

Date: 2021
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DOI: 10.1038/s41467-021-25757-3

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