摘要
电子储存环注入过程中束团参数变化剧烈,通过逐束团三维位置测量技术可实时捕捉并分析这个过程,获得横向Beta振荡和纵向同步振荡相关的多个束流动力学参数,如何利用这些测量结果来评价并指导优化加速器的运行,是同步辐射光源装置需要研究的重要问题。本文提出了一种基于机器学习的电子储存环注入品质评估及异常检测方法。选择上海光源多年累积的注入瞬态过程数据作为样本库,对补注束团的动力学参数进行聚类分析,找到装置运行状态时间维度上的演化规律并标记出不属于主群的异常数据样本。训练k-NN模型对异常数据进行了预测及校验,实验结果表明预测模型精确率好于90%,可作为装置运行性能在线自动评价的有效方法。
[Background]During electron storage ring injection,beam parameters change drastically.Bunch-bybunch three-dimensional position measurements can capture this transient process in real time to obtain multiple beam dynamics parameters.However,how to effectively utilize these measurements to evaluate and optimize accelerator operation remains a critical challenge for synchrotron radiation facilities.[Purpose]This study aims to develop a machine learning-based method for injection quality assessment and anomaly detection in electron storage rings.[Methods]Firstly,injection transient data accumulated over multiple years at the Shanghai Synchrotron Radiation Facility(SSRF)from September 2021 to June 2024 were selected as the sample database,including longitudinal oscillation amplitude,synchrotron damping time,bunch charge,and other dynamic parameters of refilled bunches.Then,the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)algorithm was applied to perform cluster analysis on the longitudinal oscillation amplitude and synchrotron damping time,identifying the temporal evolution patterns of facility operation states and labeling anomalous data samples that deviated from the main clusters.Finally,a k-Nearest Neighbor(k-NN)classification model was trained using the labeled dataset to predict anomalous injection events,with the optimal k value determined through comparative analysis.[Results]The clustering results reveal the evolution patterns of SSRF operating states over time,with Cluster 2(containing data from 2023‒2024)demonstrating significantly improved matching between the storage ring and injector compared to earlier periods.The average longitudinal oscillation amplitude decreases from 101.21 ps(before September 2023)to 64.37 ps(after September 2023),while the synchrotron damping time reduces from 3.01 ms to 1.70 ms.The optimized k-NN model with k=4 achieves a precision of 90%,recall of 66%,F1-score of 0.78,and PR-AUC of 0.84 in anomaly detection,outperforming other machine learning algorithms including Support Vector Machine,Decision Tree,Random Forest,Logistic Regression,and Naive Bayes.[Conclusions]The method proposed in this study combining unsupervised learning and supervised learning effectively detects anomalous injection events with 90%precision,enabling real-time quality assessment of storage ring injection performance.The longitudinal oscillation amplitude and synchrotron damping time serve as reliable indicators of injection quality,while the machine learning approach provides early warning capabilities for facility operators to prevent performance degradation before observable operational failures occur.
作者
方子堃
蒋天宇
周逸媚
冷用斌
FANG Zikun;JIANG Tianyu;ZHOU Yimei;LENG Yongbin(National Synchrotron Radiation Laboratory,University of Science and Technology of China,Hefei 230029,China;Shanghai Advanced Research Institute,Chinese Academy of Sciences,Shanghai 201210,China)
出处
《核技术》
北大核心
2026年第1期23-31,共9页
Nuclear Techniques
基金
中国科学院大科学装置维修改造项目“加速器束流测控通用信号处理平台”项目资助。
关键词
机器学习
电子储存环
注入瞬态
聚类分析
K-NN
Machine learning
Electron storage rings
Injection transients
Quality evaluation
k-NN
