Sprecher
Beschreibung
Plasma-based accelerators hold the potential to achieve mulit-giga-volt-per-metre accelerating gradients, offering a promising route to more compact and cost-effective accelerators for future light sources and colliders. However, plasma wakefield acceleration (PWFA) is often a nonlinear, high-dimensional process that is sensitive to jitters in multiple input parameters, making the setup, operation and diagnosis of a PWFA stage a challenging task. To tackle some of these issues, Machine Learning techniques have gained popularity in the field of plasma acceleration. Specifically, advanced algorithms such as Bayesian Optimisation have proved useful for the setup and tuning of plasma accelerators. Moreover, neural networks trained on experimental data have enabled the shot-to-shot prediction of beam parameters based on noninvasive measurements, simultaneously providing valuable insights into the different dependencies of the acceleration process. We present progress in deploying such methods at FLASHForward, a beam-driven plasma wakefield accelerator test-bed based at DESY, Hamburg, and explore future directions for further integration of these techniques at the facility.
