Speaker
Description
Increasing the performance and capabilities of free electron lasers, such as LCLS-II, hinges on our ability to precisely control and measure the 6-dimensional phase space distribution of the beam. However, conventional tomographic techniques necessitate a substantial number of measurements and computational resources to characterize a single beam distribution, using many hours of valuable beam time. In this work, we present a novel approach to analyzing experimental measurements using differentiable beam dynamics simulations and generative machine learning-based representations of 6-dimensional phase space distributions. We demonstrate in simulation and experiment that conventional beam manipulations and diagnostics can be used to effectively reconstruct detailed 6-dimensional phase space distributions using as few as 20 beam measurements with no data collection. Finally, we discuss developments in combining this work with advanced accelerator control algorithms and parasitic beam measurements to autonomously monitor the 6-dimensional phase space distribution of the beam at LCLS-II during accelerator operations.
Funding Agency
This work is supported by the U.S. Department of Energy, Office of Science under Contract No. DE-AC02-76SF00515 and the Center for Bright Beams, NSF Award No. PHY-1549132.
| Region represented | America |
|---|
