Speaker
Xinzhong Liu
(Shanghai Advanced Research Institute)
Description
A deep-learning-based feedforward scheme has been developed to compensate insertion-device (ID) effects in the Shanghai Synchrotron Radiation Facility (SSRF). Neural networks predict orbit and betatron-coupling perturbations caused by ID gap and phase changes. The orbit model reduces residual closed-orbit distortion (COD) to below 2 um and shortens preparation time by about a factor of 50 compared with conventional feedforward-table measurements. A coupling model trained with turn-by-turn (TBT) beam-position-monitor (BPM) data reaches an R^2 value above 0.95. These results show that deep learning can support fast and reproducible ID compensation during light-source operation.
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Author
Xinzhong Liu
(Shanghai Advanced Research Institute)
Co-authors
LINGLONG MAO
(Shanghai Institute of Applied Physics, Chinese Academy of Sciences)
Liyuan Tan
(Shanghai Institute of Applied Physics)
Yihao Gong
(Shanghai Synchrotron Radiation Facility)
Shunqiang Tian
(Shanghai Advanced Research Institute, Chinese Academy of Sciences)