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BiBTeX citation export for WEPV024: X-Ray Beamline Control with Machine Learning and an Online Model

@inproceedings{nash:icalepcs2021-wepv024,
  author       = {B. Nash and D.T. Abell and D.L. Bruhwiler and E.G. Carlin and Y. Du and J.P. Edelen and A. Giles and M.V. Keilman and J. Lynch and J. Maldonado and P. Moeller and R. Nagler and I.V. Pogorelov and M.S. Rakitin and A. Walter and S.D. Webb},
% author       = {B. Nash and D.T. Abell and D.L. Bruhwiler and E.G. Carlin and Y. Du and J.P. Edelen and others},
% author       = {B. Nash and others},
  title        = {{X-Ray Beamline Control with Machine Learning and an Online Model}},
  booktitle    = {Proc. ICALEPCS'21},
  pages        = {695--699},
  eid          = {WEPV024},
  language     = {english},
  keywords     = {simulation, controls, software, radiation, synchrotron},
  venue        = {Shanghai, China},
  series       = {International Conference on Accelerator and Large Experimental Physics Control Systems},
  number       = {18},
  publisher    = {JACoW Publishing, Geneva, Switzerland},
  month        = {03},
  year         = {2022},
  issn         = {2226-0358},
  isbn         = {978-3-95450-221-9},
  doi          = {10.18429/JACoW-ICALEPCS2021-WEPV024},
  url          = {https://jacow.org/icalepcs2021/papers/wepv024.pdf},
  abstract     = {{We present recent developments on control of x-ray beamlines for synchrotron light sources. Effective models of the x-ray transport are updated based on diagnostics data, and take the form of simplified physics models as well as learned models from scanning over mirror and slit configurations. We are developing this approach to beamline control in collaboration with several beamlines at the NSLS-II. By connecting our online models to the Blue-Sky framework, we enable a convenient interface between the operating machine and the model that may be applied to beamlines at multiple facilities involved in this collaborative software development.}},
}