Author: Huang, X.
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TUB04 Recent On-Line Taper Optimization on LCLS 229
  • J. Wu, X. Huang, T.O. Raubenheimer
    SLAC, Menlo Park, California, USA
  • A. Scheinker
    LANL, Los Alamos, New Mexico, USA
  Funding: The work was supported by the US Department of Energy (DOE) under contract DE-AC02-76SF00515 and the US DOE Office of Science Early Career Research Program grant FWP-2013-SLAC-100164.
High-brightness XFELs are demanding for many users, in particular for certain types of imaging applications. Self-seeding XFELs can respond to a heavily tapered undulator more effectively, therefore seeded tapered FELs are considered as a path to high-power FELs in the terawatts level. Due to many effects, including the synchrotron motion, the optimization of the taper profile is intrinsically multi-dimensional and computationally expensive. With an operating XFEL, such as LCLS, the on-line optimization becomes more economical than numerical simulation. Here we report recent on-line taper optimization on LCLS taking full advantages of nonlinear optimizers as well as up-to-date development of artificial intelligence: deep machine learning and neural networks.
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