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BiBTeX citation export for MOPA90: Relating Initial Distribution to Beam Loss on the Front End of a Heavy-Ion Linac Using Machine Learning

@inproceedings{tran:napac2022-mopa90,
  author       = {A.D. Tran and Y. Hao and J.L. Martinez Marin and B. Mustapha},
  title        = {{Relating Initial Distribution to Beam Loss on the Front End of a Heavy-Ion Linac Using Machine Learning}},
& booktitle    = {Proc. NAPAC'22},
  booktitle    = {Proc. 5th Int. Particle Accel. Conf. (NAPAC'22)},
  pages        = {263--266},
  eid          = {MOPA90},
  language     = {english},
  keywords     = {network, simulation, LEBT, emittance, controls},
  venue        = {Albuquerque, NM, USA},
  series       = {International Particle Accelerator Conference},
  number       = {5},
  publisher    = {JACoW Publishing, Geneva, Switzerland},
  month        = {10},
  year         = {2022},
  issn         = {2673-7000},
  isbn         = {978-3-95450-232-5},
  doi          = {10.18429/JACoW-NAPAC2022-MOPA90},
  url          = {https://jacow.org/napac2022/papers/mopa90.pdf},
  abstract     = {{This work demonstrates using a Neural Network and a Gaussian Process to model the ATLAS front-end. Various neural network architectures were created and trained on the machine settings and outputs to model the phase space projections. The model was then trained on a dataset, with non-linear distortion, to gauge the transferability of the model from simulation to machine.}},
}