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TY - CONF AU - Leemann, S.C. AU - Amstutz, Ph. AU - Ehrlichman, M.P. AU - Hellert, T. AU - Hexemer, A. AU - Liu, S. 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. ED - Boland, Mark ED - Tanaka, Hitoshi ED - Button, David ED - Dowd, Rohan ED - Schaa, Volker RW ED - Tan, Eugene TI - First Attempts at Applying Machine Learning to ALS Storage Ring Stabilization J2 - Proc. of IPAC2019, Melbourne, Australia, 19-24 May 2019 CY - Melbourne, Australia T2 - International Particle Accelerator Conference T3 - 10 LA - english AB - 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. PB - JACoW Publishing CP - Geneva, Switzerland SP - 1631 EP - 1634 KW - experiment KW - quadrupole KW - storage-ring KW - operation KW - emittance DA - 2019/06 PY - 2019 SN - 978-3-95450-208-0 DO - DOI: 10.18429/JACoW-IPAC2019-TUPGW094 UR - http://jacow.org/ipac2019/papers/tupgw094.pdf ER -