AI 中文总结
研究现代实验物理中粒子加速器等复杂仪器面临的设计与运行挑战，利用机器学习技术分析相关大数据以挖掘故障见解，还能加速/增强设计阶段物理模拟，助力仪器性能提升与目标实现。
AI 中文摘要
在现代实验物理学中，粒子加速器和引力波天文台推动了前沿科学的广泛研究。这些仪器高度复杂，由许多相互作用的系统组成，面临重大运行挑战。除实验主要数据产品外，还记录了大量关于实验装置及其环境的数据。机器学习技术可大规模分析这些大数据，挖掘运行故障的有用见解，可能改善仪器性能并实现设计目标。在设计方面，机器学习还能加速/增强此类大型仪器设计阶段使用的昂贵物理模拟。
英文摘要
In modern experimental physics, particle accelerators and gravitational-wave observatories enable a wide-range of research at the frontiers of science. These instruments are highly complex consisting of many interacting systems which can face significant operational challenges. Apart from the experiment's main data product, a lot of data about the experimental apparatus and its environment is recorded. Machine learning techniques can analyze this big data at scale and find useful insights into operational faults potentially improving the instrument's performance and achieving the design goals. Speaking of design, machine learning can also accelerate/augment the expensive physics simulations used during the design phase of such large-scale instruments.
Comments28 pages, 8 figures
Journal refArtificial Intelligence for Science, 95-116 (2023)
DOI：10.1142/9789811265679_0007
