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

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.

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

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.

Demonstration of Model-Independent Control of the Longitudinal Phase Space of Electron Beams in the Linac-Coherent Light Source with Femtosecond Resolution.

The dynamics of intense electron bunches in free electron lasers and plasma wakefield accelerators are dominated by complex collective effects such as wakefields, space charge, coherent synchrotron radiation, and drift unpredictably with time, making it difficult to control and tune beam properties using model-based approaches. We report on a first of its kind combination of automatic, model-independent feedback with a neural network for control of the longitudinal phase space of relativistic electron beams with femtosecond resolution based only on transverse deflecting cavity measurements.