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BiBTeX citation export for THZE1: Machine Learning-Based Longitudinal Phase Space Prediction of Particle Accelerators

@unpublished{emma:napac2022-thze1,
  author       = {C. Emma},
  title        = {{Machine Learning-Based Longitudinal Phase Space Prediction of Particle Accelerators}},
% booktitle    = {Proc. NAPAC'22},
  booktitle    = {Proc. 5th Int. Particle Accel. Conf. (NAPAC'22)},
  language     = {english},
  intype       = {presented at the},
  series       = {International Particle Accelerator Conference},
  number       = {5},
  venue        = {Albuquerque, NM, USA},
  publisher    = {JACoW Publishing, Geneva, Switzerland},
  month        = {10},
  year         = {2022},
  note         = {presented at NAPAC'22 in Albuquerque, NM, USA, unpublished},
  abstract     = {{The author would report on the application of machine learning (ML) methods for predicting the longitudinal phase space (LPS) distribution of particle accelerators. The approach consists of training a ML-based virtual diagnostic to predict the LPS using only nondestructive linac and e-beam measurements as inputs.}},
}