Sprecher
Beschreibung
Plasma acceleration has seen tremendous progress over the past years demonstrating competitive beam quality from compact setups. However, plasma accelerators live on a very complex non-linear parameter space, which makes it very challening to, first, identify an optimum working point, and then, second, to operate the plasma accelerator reliably at this point with reproducible beams.
The advent of high-repetition plasma accelerators, powered by a new generation of drive lasers or high repetition rate electron beams, addresses this challenge by enabling advanced control techniques, active stabilization and feedback, as deployed in any modern accelerator facility.
Here, we will discuss our recent activities at DESY, developing machine-learning-assisted software tools to design and optimize plasma accelerator setups, and discuss the deployment of machine-learning technqiues to enhance the performance of beam- and laser-driven plasma accelerator experiments.
