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

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

Characterizing an unknown, complex system, like an accelerator, in multi-dimensional space is a challenging task. Here the authors report a Bayesian active learning method - Constrained Proximal Bayesian Exploration - for the characterization of a complex, constrained measurement as a function of mu...

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Bibliographic Details
Main Authors: Ryan Roussel, Juan Pablo Gonzalez-Aguilera, Young-Kee Kim, Eric Wisniewski, Wanming Liu, Philippe Piot, John Power, Adi Hanuka, Auralee Edelen
Format: Article
Language:English
Published: Nature Publishing Group 2021-09-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-021-25757-3
Description
Summary:Characterizing an unknown, complex system, like an accelerator, in multi-dimensional space is a challenging task. Here the authors report a Bayesian active learning method - Constrained Proximal Bayesian Exploration - for the characterization of a complex, constrained measurement as a function of multiple free parameters.
ISSN:2041-1723

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