Joint Accelerator Conferences Website

The Joint Accelerator Conferences Website (JACoW) is an international collaboration that publishes the proceedings of accelerator conferences held around the world.

BiBTeX citation export for THCPL03: Machine Learning for Beam Size Stabilization at the Advanced Light Source

@unpublished{melton:icalepcs2019-thcpl03,
  author       = {C.N. Melton and A. Hexemer and S.C. Leemann and S. Liu and M. Marcus and H. Nishimura and C. Sun},
% author       = {C.N. Melton and A. Hexemer and S.C. Leemann and S. Liu and M. Marcus and H. Nishimura and others},
% author       = {C.N. Melton and others},
  title        = {{Machine Learning for Beam Size Stabilization at the Advanced Light Source}},
  booktitle    = {Proc. ICALEPCS'19},
  language     = {english},
  intype       = {presented at the},
  series       = {International Conference on Accelerator and Large Experimental Physics Control Systems},
  number       = {17},
  venue        = {New York, NY, USA},
  publisher    = {JACoW Publishing, Geneva, Switzerland},
  month        = {aug},
  year         = {2020},
  note         = {presented at ICALEPCS2019 in New York, NY, USA, unpublished},
  abstract     = {Synchrotron beam size stability is a necessity in producing reliable, repeatable, and novel experiments at bright light source facilities such as the Advanced Light Source (ALS). As both brightness and coherence are set to increase drastically through upgrades at such facilities, current methods to ensure beam size stabilization will soon reach their limit. Current beam size stability is on the order of several microns (few percent) and is achieved by a combination of feedbacks, physical models, and feed-forward look-up tables to counteract lattice imperfections and optics perturbations arising from varying insertion device gaps and phases. In this work we highlight our first attempts to implement machine learning to stabilize the beam size at the ALS. The use of neural networks allows for beam size stabilization not dependent on physical models, but instead using insertion device movement as training input. Such a correction model can be continuously retrained via online methods. This method results in beam size stabilization as low as 0.2 microns rms, an order of magnitude lower than current stabilization methods.},
}