Speaker
Guimei Wang
(Brookhaven National Laboratory)
Description
The NSLS-II injector comprises a linear accelerator (LINAC) that accelerates electron beam to 170 MeV, followed by the linac-to-booster (LTB) transport line and a booster synchrotron that increases the beam energy to 3 GeV. The performance of LINAC and LTB is critical for efficient injection. Automated online tuning has proven effective for improving injector performance. The conventional approach optimizes the LINAC and LTB separately, while requiring multiple iterations. This paper presents results from an integrated automated tuning approach that applies reinforcement learning and machine learning techniques to simultaneously optimize the LINAC and LTB for enhanced injection performance.
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Author
Minghao Song
(Brookhaven National Laboratory)
Co-authors
Yoshiteru Hidaka
(Brookhaven National Laboratory)
Guimei Wang
(Brookhaven National Laboratory)
Xi Yang
(Brookhaven National Laboratory)