CANCELED!! Adaptive Feedback Control and Machine Learning for Particle Accelerators

by Alexander Scheinker (LBNL)

Tuesday 17 Mar 2020, 15:00 → 16:00 Europe/Zurich

WBGB/019 (PSI)

The precise control of charged particle beams, such as an electron beam's longitudinal
phase space, (current and energy profiles), as well as the maximization of the output power of a
free electron laser (FEL), or the minimization of beam loss in accelerators, are extremely
challenging tasks. For example, even when all FEL parameter set points are held constant both
the beam phase space and the output power have high variance because of the uncertainty and
time-variation of thousands of coupled parameters and of the electron distribution coming off of
the photo cathode. Similarly, all large accelerators face challenges due to time variation, leading
to beam losses and changing behavior even when all accelerator parameters are held fixed. We
present the development and application of machine learning methods along with automatic,
model-independent feedback for automatic tuning of charge particle beams in particle
accelerators. We present experimental results from the LANSCE linear accelerator at LANL, the
EuXFEL at DESY, the AWAKE experiment at CERN, the SPEAR3 light source at SLAC, the
FACET plasma wakefield accelerator facility at SLAC, and at the LCLS FEL at SLAC.

For details, contact Alexander Malyzhenkov

Flyer Scheinker 17Mar20.pdf