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BiBTeX citation export for THAL04: Machine Learning Based Tuning and Diagnostics for the ATR Line at BNL

  author       = {J.P. Edelen and K.A. Brown and K. Bruhwiler and E.G. Carlin and C.C. Hall and V. Schoefer},
  title        = {{Machine Learning Based Tuning and Diagnostics for the ATR Line at BNL}},
  booktitle    = {Proc. ICALEPCS'21},
  pages        = {803--808},
  eid          = {THAL04},
  language     = {english},
  keywords     = {quadrupole, network, simulation, controls, diagnostics},
  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-THAL04},
  url          = {https://jacow.org/icalepcs2021/papers/thal04.pdf},
  abstract     = {{Over the past several years machine learning has increased in popularity for accelerator applications. We have been exploring the use of machine learning as a diagnostic and tuning tool for transfer line from the AGS to RHIC at Brookhaven National Laboratory. In our work, inverse models are used to either provide feed-forward corrections for beam steering or as a diagnostic to illuminate quadrupole magnets that have excitation errors. In this talk we present results on using machine learning for beam steering optimization for a range of different operating energies. We also demonstrate the use of inverse models for optical error diagnostics. Our results are from studies that use combine simulation and measurement data.}},