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BiBTeX citation export for MOPOTK013: Machine Learning Based Surrogate Model Construction for Optics Matching at the European XFEL

@inproceedings{zhu:ipac2022-mopotk013,
  author       = {Z.H. Zhu and Y. Chen and W. Qin and M. Scholz and S. Tomin},
  title        = {{Machine Learning Based Surrogate Model Construction for Optics Matching at the European XFEL}},
  booktitle    = {Proc. IPAC'22},
% booktitle    = {Proc. 13th International Particle Accelerator Conference (IPAC'22)},
  pages        = {461--464},
  eid          = {MOPOTK013},
  language     = {english},
  keywords     = {optics, simulation, quadrupole, electron, FEL},
  venue        = {Bangkok, Thailand},
  series       = {International Particle Accelerator Conference},
  number       = {13},
  publisher    = {JACoW Publishing, Geneva, Switzerland},
  month        = {07},
  year         = {2022},
  issn         = {2673-5490},
  isbn         = {978-3-95450-227-1},
  doi          = {10.18429/JACoW-IPAC2022-MOPOTK013},
  url          = {https://jacow.org/ipac2022/papers/mopotk013.pdf},
  abstract     = {{Beam optics matching is a daily routine in the operation of an X-ray free-electron laser facility. Usually, linear optics is employed to conduct the beam matching in the control room. However, the collective effects like space charge dominate the electron bunch in the low-energy region which decreases the accuracy of the existing tool. Therefore, we proposed a scheme to construct a surrogate model with nonlinear optics and collective effects to speed up the optics matching in the European XFEL injector section. This model also facilitates further research on beam dynamics for the space-charge dominated beam.}},
}