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TY - UNPB AU - Leemann, S.C. ED - Tavares, Daniel ED - Picoreti, Renan ED - Bruno, Gustavo ED - Marques, Sergio ED - Schaa, Volker R.W. TI - Machine Learning-based Beam Size Stabilization J2 - Proc. of IBIC2020, Santos, Brazil, 14-18 September 2020 CY - Santos, Brazil T2 - International Beam Instrumentation Conference T3 - 9 LA - english AB - In state-of-the-art synchrotron light sources the overall source stability is presently limited by the achievable level of electron beam size stability. This source size stability is presently on the few-percent level, which is still 1-2 orders of magnitude larger than already demonstrated stability of source position/angle (slow/fast orbit feedbacks) and current (top-off injection). Until now, source size stabilization has been achieved through corrections based on a combination of static predetermined physics models and lengthy calibration measurements (feed-forward tables), periodically repeated to counteract drift in the accelerator and instrumentation. We now demonstrate for the first time [PRL 123 194801 (2019)], how application of machine learning allows for a physics- and model-independent stabilization of source size relying only on previously existing instrumentation in ALS. Such feed-forward correction based on neural networks that can be continuously online-retrained achieves source size stability as low as 0.2 microns rms (0.4%) which results in overall source stability approaching the sub-percent noise floor of the most sensitive experiments. PB - JACoW Publishing CP - Geneva, Switzerland ER -