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@unpublished{leemann:ibic2020-thao01, author = {S.C. Leemann}, title = {{Machine Learning-based Beam Size Stabilization}}, booktitle = {Proc. IBIC'20}, language = {english}, intype = {presented at the}, series = {International Beam Instrumentation Conference}, number = {9}, venue = {Santos, Brazil}, publisher = {JACoW Publishing, Geneva, Switzerland}, month = {oct}, year = {2020}, note = {presented at IBIC2020 in Santos, Brazil, unpublished}, abstract = {In state-of-the-art synchrotron light sources the overall source stability is presently limited by the achievable level of electron beam size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.}, }