Author: Marcus, M.
Paper Title Page
Machine Learning for Beam Size Stabilization at the Advanced Light Source  
  • C.N. Melton, A. Hexemer, S.C. Leemann, S. Liu, M. Marcus, H. Nishimura, C. Sun
    LBNL, Berkeley, California, USA
  Funding: This research is funded by US Department of Energy (BES & ASCR Programs), and supported by the Director of the Office of Science of the US Department of Energy under Contract No. DEAC02-05CH11231.
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.
slides icon Slides THCPL03 [3.388 MB]  
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