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BiBTeX citation export for TUIXGD1: Accurate and Confident Prediction of Electron Beam Longitudinal Properties Using Spectral Virtual Diagnostics

@unpublished{hanuka:ipac2022-tuixgd1,
  author       = {A. Hanuka},
  title        = {{Accurate and Confident Prediction of Electron Beam Longitudinal Properties Using Spectral Virtual Diagnostics}},
  booktitle    = {Proc. IPAC'22},
  language     = {english},
  intype       = {presented at the},
  series       = {International Particle Accelerator Conference},
  number       = {13},
  venue        = {Bangkok, Thailand},
  publisher    = {JACoW Publishing, Geneva, Switzerland},
  month        = {07},
  year         = {2022},
  note         = {presented at IPAC'22 in Bangkok, Thailand, unpublished},
  abstract     = {{Longitudinal phase space measurement is one of the key diagnostics in FEL machines. In this work it is shown as the application of machine learning based virtual diagnostics can provide undirect information on the longitudinal phase space of the beam in a non intercepting way and, in combination with standard longitudinal diagnostics methods, helps overcoming resolution limitations and increasing the accuracy of the measured data.}},
}