Speaker
Description
Particle accelerators are large, complex and time-varying machines with limited diagnostics and time-varying and uncertain charged particle beams, making it difficult to perform automatic and model-based tuning. Free electron lasers (FEL) and plasma wakefield accelerators (PWFA) are creating more and more complicated electron bunch configurations, including multi-color modes for FELs such as LCLS and LCLS-II and custom tailored bunch current profiles for PWAs such as FACET-II. These accelerators are also producing shorter and higher intensity bunches than before whose dynamics experience complex collective effects such as intense space charge forces and CSR. FELs and PWFAs require an ability to quickly switch between many different users with various phase space requirements,in practice exotic setups require lengthy tuning. This talk discusses machine learning (ML) and model independent feedback techniques and their application in both the LCLS and European XFEL to maximize the average pulse output energy of FELs by automatically tuning over 100 components simultaneously. We also discuss the creation of non-invasive longitudinal phase space (LPS) diagnostics at PWAs. Finally, we present a hybrid adaptive ML approach in which model-independent methods together with neural networks which has been demonstrated at the LCLS to control electron bunch LPS by tuning FEL components automatically.
