Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction

Fuhao Ji (), Auralee Edelen, Ryan Roussel, Xiaozhe Shen, Sara Miskovich, Stephen Weathersby, Duan Luo, Mianzhen Mo, Patrick Kramer, Christopher Mayes, Mohamed A. K. Othman, Emilio Nanni, Xijie Wang, Alexander Reid, Michael Minitti and Robert Joel England ()
Additional contact information
Fuhao Ji: SLAC National Accelerator Laboratory
Auralee Edelen: SLAC National Accelerator Laboratory
Ryan Roussel: SLAC National Accelerator Laboratory
Xiaozhe Shen: SLAC National Accelerator Laboratory
Sara Miskovich: SLAC National Accelerator Laboratory
Stephen Weathersby: SLAC National Accelerator Laboratory
Duan Luo: SLAC National Accelerator Laboratory
Mianzhen Mo: SLAC National Accelerator Laboratory
Patrick Kramer: SLAC National Accelerator Laboratory
Christopher Mayes: SLAC National Accelerator Laboratory
Mohamed A. K. Othman: SLAC National Accelerator Laboratory
Emilio Nanni: SLAC National Accelerator Laboratory
Xijie Wang: SLAC National Accelerator Laboratory
Alexander Reid: SLAC National Accelerator Laboratory
Michael Minitti: SLAC National Accelerator Laboratory
Robert Joel England: SLAC National Accelerator Laboratory

Nature Communications, 2024, vol. 15, issue 1, 1-7

Abstract: Abstract Ultrafast electron diffraction using MeV energy beams(MeV-UED) has enabled unprecedented scientific opportunities in the study of ultrafast structural dynamics in a variety of gas, liquid and solid state systems. Broad scientific applications usually pose different requirements for electron probe properties. Due to the complex, nonlinear and correlated nature of accelerator systems, electron beam property optimization is a time-taking process and often relies on extensive hand-tuning by experienced human operators. Algorithm based efficient online tuning strategies are highly desired. Here, we demonstrate multi-objective Bayesian active learning for speeding up online beam tuning at the SLAC MeV-UED facility. The multi-objective Bayesian optimization algorithm was used for efficiently searching the parameter space and mapping out the Pareto Fronts which give the trade-offs between key beam properties. Such scheme enables an unprecedented overview of the global behavior of the experimental system and takes a significantly smaller number of measurements compared with traditional methods such as a grid scan. This methodology can be applied in other experimental scenarios that require simultaneously optimizing multiple objectives by explorations in high dimensional, nonlinear and correlated systems.

Date: 2024
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DOI: 10.1038/s41467-024-48923-9

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