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DOE OSTI · 2331235

EBIS Beam Intensity Online Optimization with GPTune and Offline Analysis with XGBoost

Gu, Xiaofeng·Kanesue, Takeshi·Okamura, Masahiro·Hao, Yue·Qiang, Ji·Li, Xiaoye S.·Liu, Yang
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Abstract

The application of machine learning techniques to accelerator research has led to significant breakthroughs in optimization strategies. This paper presents a pioneering study using a novel machine learning algorithm, GPTune, to optimize beam intensity by adjusting parameters in the EBIS injection and extraction beam lines. Our research demonstrates substantial improvements, achieving a remarkable 22% and 70% increase in beam intensity at two separate measurement locations.

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Gu, Xiaofeng, Kanesue, Takeshi, Okamura, Masahiro, Hao, Yue, Qiang, Ji, Li, Xiaoye S., Liu, Yang. 2024-02-28. EBIS Beam Intensity Online Optimization with GPTune and Offline Analysis with XGBoost. https://doi.org/10.2172/2331235

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