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BiBTeX citation export for WEVIR10: Adaptive Feedback Control and Machine Learning for Particle Accelerators

@InProceedings{scheinker:ipac2020-wevir10,
  author       = {A. Scheinker},
  title        = {{Adaptive Feedback Control and Machine Learning for Particle Accelerators}},
  booktitle    = {Proc. IPAC'20},
  pages        = {53--56},
  paper        = {WEVIR10},
  language     = {english},
  keywords     = {FEL, electron, controls, diagnostics, feedback},
  venue        = {Caen, France},
  series       = {International Particle Accelerator Conference},
  number       = {11},
  publisher    = {JACoW Publishing, Geneva, Switzerland},
  month        = {10},
  year         = {2020},
  issn         = {2673-5490},
  isbn         = {978-3-95450-213-4},
  doi          = {10.18429/JACoW-IPAC2020-WEVIR10},
  url          = {http://jacow.org/ipac2020/papers/wevir10.pdf},
  note         = {https://doi.org/10.18429/JACoW-IPAC2020-WEVIR10},
  abstract     = {The precise control of charged particle beams, such as an electron beam’s longitudinal phase space as well as the maximization of the output power of a free electron laser (FEL), or the minimization of beam loss in accelerators, are 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 recent efforts towards developing 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, AWAKE at CERN, FACET-II and the LCLS.},
}