A non-invasive diagnostic method of cavity detuning based on a convolutional neural network  

作　　者：Liu-Yuan Zhou Hao Zha Jia-Ru Shi Jia-Qi Qiu Chuan-Jing Wang Yun-Sheng Han Huai-Bi Chen 

机构地区：[1]Department of Engineering Physics,Tsinghua University,Beijing,100084,China [2]Key Laboratory of Particle and Radiation Imaging,Tsinghua University,Beijing,100084,China

出　　处：《Nuclear Science and Techniques》2022年第7期25-35,共11页核技术（英文）

基　　金：supported by the National Natural Science Foundation of China(No.11922504).

摘　　要：As modern accelerator technologies advance toward more compact sizes,conventional invasive diagnostic methods of cavity detuning introduce negligible interference in measurements and run the risk of harming structural surfaces.To overcome these difficulties,this study developed a non-invasive diagnostic method using knowledge of scattering parameters with a convolutional neural network and the interior point method.Meticulous construction and training of the neural network led to remarkable results on three typical acceleration structures:a 13-cell S-band standing-wave linac,a 12-cell X-band traveling-wave linac,and a 3-cell X-band RF gun.The trained networks significantly reduced the burden of the tuning process,freed researchers from tedious tuning tasks,and provided a new perspective for the tuning of side-coupling,semi-enclosed,and total-enclosed structures.

关 键 词：Cavity detuning Convolutional neural network Equivalent circuit 

分 类 号：TL50[核科学技术—核技术及应用] TP183[自动化与计算机技术—控制理论与控制工程]