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

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 me

SUBMITTER: Roussel R 

PROVIDER: S-EPMC8460822 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature