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BiBTeX citation export for TUPA65: Machine Learning for the LANL Electromagnetic Isotope Separator

@inproceedings{scheinker:napac2022-tupa65,
  author       = {A. Scheinker and K.W. Dudeck and C.P. Leibman},
  title        = {{Machine Learning for the LANL Electromagnetic Isotope Separator}},
& booktitle    = {Proc. NAPAC'22},
  booktitle    = {Proc. 5th Int. Particle Accel. Conf. (NAPAC'22)},
  pages        = {490--492},
  eid          = {TUPA65},
  language     = {english},
  keywords     = {controls, dipole, ion-source, feedback, electron},
  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-TUPA65},
  url          = {https://jacow.org/napac2022/papers/tupa65.pdf},
  abstract     = {{The Los Alamos National Laboratory electromagnetic isotope separator (EMIS) utilizes a Freeman ion source to generate beams of various elements which are accelerated to 40 keV and passed through a 75-degree bend using a large dipole magnet with a radius of 1.2 m. The isotope mass differences translate directly to a spread in momentum, dp, relative to the design momentum p0. Momentum spread is converted to spread in the horizontal arrival location dx at a target chamber by the dispersion of the dipole magnet: dx = D(s)dp/p0. By placing a thin slit leading to a collection chamber at a location xc specific isotope mass is isolated by adjusting the dipole magnet strength or the beam energy. The arriving beam current at xc is associated with average isotope atomic mass, giving an isotope mass spectrum I(m) measured in mA. Although the EMIS is a compact system (5 m) setting up and automatically running at an optimal isotope separation profile I(m) profile is challenging due to time-variation of the complex source as well as un-modeled disturbances. We present preliminary results of developing adaptive machine learning-based tools for the EMIS beam and for the accelerator components.}},
}