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BiBTeX citation export for TUPA34: Model-Based Calibration of Control Parameters at the Argonne Wakefield Accelerator

@inproceedings{sugrue:napac2022-tupa34,
  author       = {I.P. Sugrue and N. Krislock and B. Mustapha and P. Piot and J.G. Power},
  title        = {{Model-Based Calibration of Control Parameters at the Argonne Wakefield Accelerator}},
& booktitle    = {Proc. NAPAC'22},
  booktitle    = {Proc. 5th Int. Particle Accel. Conf. (NAPAC'22)},
  pages        = {427--429},
  eid          = {TUPA34},
  language     = {english},
  keywords     = {network, controls, gun, simulation, wakefield},
  venue        = {Albuquerque, NM, USA},
  series       = {International Particle Accelerator Conference},
  number       = {5},
  publisher    = {JACoW Publishing, Geneva, Switzerland},
  month        = {10},
  year         = {2022},
  issn         = {2673-7000},
  isbn         = {978-3-95450-232-5},
  doi          = {10.18429/JACoW-NAPAC2022-TUPA34},
  url          = {https://jacow.org/napac2022/papers/tupa34.pdf},
  abstract     = {{Particle accelerators utilize a large number of control parameters to generate and manipulate beams. Digital models and simulations are often used to find the best operating parameters to achieve a set of given beam parameters. Unfortunately, the optimized physics parameters cannot precisely be set in the control system due to, e.g., calibration uncertainties. We developed a data-driven physics-informed surrogate model using neural networks to replace digital models relying on beam-dynamics simulations. This surrogate model can then be used to perform quick diagnostics of the Argonne Wakefield accelerator in real time using nonlinear least-squares methods to find the most likely operating parameters given a measured beam distribution.}},
}