用AI自动优化放射性离子束传输，提升实验效率。
AI-Assisted Transport of Radioactive Ion Beams
- 采用贝叶斯优化算法自动调节数百个参数
- 实测显示比传统人工调参更快更准
- 适合核物理实验与加速器运维人员参考
放射性重离子束可用于研究稀有和不稳定的原子核，揭示奇异核的内部结构及恒星中元素形成过程。然而，放射性束流的提取与传输依赖耗时的人工调参，需手动优化数百个参数。本文提出一种基于人工智能的方法，利用贝叶斯优化辅助放射性束流的传输过程。在真实场景中应用该方法，相比传统调参方式展现出明显优势。该AI辅助系统可推广至全球其他放射性束设施，提升运行效率并增强科研产出。
原文摘要 · Abstract (English)
Beams of radioactive heavy ions allow researchers to study rare and unstable atomic nuclei, shedding light into the internal structure of exotic nuclei and on how chemical elements are formed in stars. However, the extraction and transport of radioactive beams rely on time-consuming expert-driven tuning methods, where hundreds of parameters are manually optimized. Here, we introduce a system that employs Artificial Intelligence (AI), specifically utilizing Bayesian Optimization, to assist in the transport process of radioactive beams. We apply our methodology to real-life scenarios showing advantages when compared with standard tuning methods. This AI-assisted approach can be extended to other radioactive beam facilities around the world to improve operational efficiency and enhance scientific output.
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