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

• Published in: Nuclear science and techniques Vol. 33; no. 7; pp. 143 - 153
• Main Authors: Zhou, Liu-Yuan, Zha, Hao, Shi, Jia-Ru, Qiu, Jia-Qi, Wang, Chuan-Jing, Han, Yun-Sheng, Chen, Huai-Bi
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.07.2022; Department of Engineering Physics,Tsinghua University,Beijing 100084,China; Key Laboratory of Particle and Radiation Imaging,Tsinghua University,Beijing 100084,China
• Subjects: Beam Physics; Nuclear Energy; Particle Acceleration and Detection; Particle and Nuclear Physics; Physics; Physics and Astronomy; Convolutional neural network; Cavity detuning; Equivalent circuit
• ISSN: 1001-8042; 2210-3147
• DOI: 10.1007/s41365-022-01069-z

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.