THCPL03
LBNL, Berkeley, California, USA
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.
