  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.
