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BiBTeX citation export for WEPV020: Learning to Lase: Machine Learning Prediction of FEL Beam Properties

  author       = {A.E. Pollard and D.J. Dunning and M. Maheshwari},
  title        = {{Learning to Lase: Machine Learning Prediction of FEL Beam Properties}},
  booktitle    = {Proc. ICALEPCS'21},
  pages        = {677--680},
  eid          = {WEPV020},
  language     = {english},
  keywords     = {network, diagnostics, simulation, FEL, electron},
  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-WEPV020},
  url          = {https://jacow.org/icalepcs2021/papers/wepv020.pdf},
  abstract     = {{Accurate prediction of longitudinal phase space and other properties of the electron beam are computationally expensive. In addition, some diagnostics are destructive in nature and/or cannot be readily accessed. Machine learning based virtual diagnostics can allow for the real-time generation of longitudinal phase space and other graphs, allowing for rapid parameter searches, and enabling operators to predict otherwise unavailable beam properties. We present a machine learning model for predicting a range of diagnostic screens along the accelerator beamline of a free-electron laser facility, conditional on linac and other parameters. Our model is a combination of a conditional variational autoencoder and a generative adversarial network, which generates high fidelity images that accurately match simulation data. Work to date is based on start-to-end simulation data, as a prototype for experimental applications.}},