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@InProceedings{leemann:napac2019-moplm04, author = {S.C. Leemann and Ph. Amstutz and W.E. Byrne and M.P. Ehrlichman and T. Hellert and A. Hexemer and S. Liu and M. Marcus and C.N. Melton and H. Nishimura and G. Penn and F. Sannibale and D.A. Shapiro and C. Sun and D. Ushizima and M. Venturini}, % author = {S.C. Leemann and Ph. Amstutz and W.E. Byrne and M.P. Ehrlichman and T. Hellert and A. Hexemer and others}, % author = {S.C. Leemann and others}, title = {{First Attempts at Applying Machine Learning to ALS Storage Ring Stabilization}}, booktitle = {Proc. NAPAC'19}, pages = {98--101}, paper = {MOPLM04}, language = {english}, keywords = {experiment, quadrupole, storage-ring, emittance, operation}, venue = {Lansing, MI, USA}, series = {North American Particle Accelerator Conference}, number = {4}, publisher = {JACoW Publishing, Geneva, Switzerland}, month = {10}, year = {2019}, issn = {2673-7000}, isbn = {978-3-95450-223-3}, doi = {10.18429/JACoW-NAPAC2019-MOPLM04}, url = {http://jacow.org/napac2019/papers/moplm04.pdf}, note = {https://doi.org/10.18429/JACoW-NAPAC2019-MOPLM04}, abstract = {The ALS storage ring operates multiple feedbacks and feed-forwards during user operations to ensure that various source properties such as beam position, beam angle, and beam size are maintained constant. Without these active corrections, strong perturbations of the electron beam would result from constantly varying ID gaps and phases. An important part of the ID gap/phase compensation requires recording feed-forward tables. While recording such tables takes a lot of time during dedicated machine shifts, the resulting compensation data is imperfect due to machine drift both during and after recording of the table. Since it is impractical to repeat recording feed-forward tables on a more frequent basis, we have decided to employ Machine Learning techniques to improve ID compensation in order to stabilize electron beam properties at the source points.}, }