理研RIBFにおける機械学習を用いた大強度重イオンビームの輸送最適化のための研究

抄録

Accelerator facilities require the control of numerous parameters; at the REKEN RI Beam Factory (RIBF), a complex of cyclotrons and linacs, more than 600 parameters—including environmental factors—affect beam quality. To optimize them more efficiently, we have been developing Bayesian optimization (BO) techniques, focusing on indices suitable for high-intensity beams and methods that maintain operational safety. We established a technique to measure beam transmission and spot size simultaneously using charge-converted particles downstream of the target, and we are also studying line BO with safety functions, so called SafeLineBO. This article discusses the major challenges, practical considerations, and failure cases encountered when applying machine-learning-based optimization to real accelerator operations.