Speaker
Xiaofeng Gu
(Brookhaven National Laboratory)
Description
The utilization of machine learning techniques in accelerator research has yielded remarkable advancements in optimization strategies. This paper presents a pioneering study employing a machine learning algorithm, GPTune, to optimize beam intensity by adjusting parameters within the EBIS injection and extraction beam lines. Demonstrating significant enhancements, our research showcases a remarkable 22% and 70% improvements in beam intensity at two different measurement locations.
| Region represented | North America |
|---|---|
| Paper preparation format | LaTeX |
Primary author
Xiaofeng Gu
(Brookhaven National Laboratory)
Co-authors
Benjamin Coe
(Brookhaven National Laboratory)
Ji Qiang
(Lawrence Berkeley National Laboratory)
Masahiro Okamura
(Brookhaven National Laboratory)
Takeshi Kanesue
(Brookhaven National Laboratory)
Xiaoye Li
(Lawrence Berkeley National Laboratory)
Yang Liu
(Lawrence Berkeley National Laboratory)
Yue Hao
(Facility for Rare Isotope Beams)
