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RIS citation export for WEPGW058: Orbit Correction With Machine Learning

AU  - Xiao, D.J.
AU  - Chu, C.P.
AU  - Qiao, Y.S.
ED  - Boland, Mark
ED  - Tanaka, Hitoshi
ED  - Button, David
ED  - Dowd, Rohan
ED  - Schaa, Volker RW
ED  - Tan, Eugene
TI  - Orbit Correction With Machine Learning
J2  - Proc. of IPAC2019, Melbourne, Australia, 19-24 May 2019
CY  - Melbourne, Australia
T2  - International Particle Accelerator Conference
T3  - 10
LA  - english
AB  - Orbit correction is usually an important task in the operation of accelerators. In practice, due to various errors, many devices can not operate in ideal state. By correcting the errors of magnets with corrector magnets, the beam can return to the correct position to ensure the stable operation of the accelerator. In the process of orbit correction, inaccurate BPM output will lead to incorrect correction magnet strength setting, so that the orbit correction will be impacted. BPM may make mistakes in the process of signal acquisition and current conversion. A BPM anomaly detection and predict method based on machine learning and its using in orbit correction optimization is reported in this paper. This method does not need to observe the details of BPM system, electronics technology and so on. It can monitor and predict the BPM status directly by machine learning with the information of the beam inferred from BPM and others, and optimize the orbit correction.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 2608
EP  - 2610
KW  - lattice
KW  - storage-ring
KW  - closed-orbit
KW  - simulation
KW  - coupling
DA  - 2019/06
PY  - 2019
SN  - 978-3-95450-208-0
DO  - DOI: 10.18429/JACoW-IPAC2019-WEPGW058
UR  - http://jacow.org/ipac2019/papers/wepgw058.pdf
ER  -