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TY - UNPB AU - Leemann, S.C. AU - Byrne, W.E. AU - Cuneo, D.P. AU - Ehrlichman, M.P. AU - Hellert, T. AU - Hexemer, A. AU - Lu, Y. AU - Marcus, M. AU - Melton, C.N. AU - Nishimura, H. AU - Penn, G. AU - Sannibale, F. AU - Shapiro, D.A. AU - Sun, C. AU - Ushizima, D. AU - Venturini, M. AU - Wallén, E.J. ED - Seidel, Mike ED - Aßmann, Ralph W. ED - Chautard, Frédéric ED - Schaa, Volker R.W. TI - Applying Machine Learning to Stabilize the Source Size in the ALS Storage Ring J2 - Proc. of IPAC2020, Caen, France, 10-15 May 2020 CY - Caen, France T2 - International Particle Accelerator Conference T3 - 11 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* 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 -