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RIS citation export for TUPGW094: First Attempts at Applying Machine Learning to ALS Storage Ring Stabilization

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  -