We report on the application of machine learning (ML) methods for predicting and optimizing the e-beam distribution of particle accelerators, with a focus on proof-of-principle studies aimed at future applications in the FACET-II linac (including longitudinal phase space prediction and round-to-flat beam transforms). The approach consists of training ML-based virtual diagnostics to predict the...
A tunable plasma-based energy dechirper has been developed at FLASHForward to remove the correlated energy spread of a 681 MeV electron bunch. Through the interaction of the bunch with wakefields excited in plasma the projected energy spread was reduced from a FWHM of 1.31% to 0.33% without reducing the stability of the incoming beam. The experimental results for variable plasma density are in...
