IPAC'23 - Student Poster Session Guide

IPAC’23 / STUDENT POSTER SESSION GUIDE 24 Student Poster Session SUPM037 Study on the Laser Treatment of Nb Thin Films on Copper Substrate with a kW nanosecond fiber laser ChangLin Wang , Didi Luo, Pingran Xiong, Qingwei Chu, Teng Tan (Institute of Modern Physics, Chi- nese Academy of Sciences). Surface annealing using intense nanosecond laser pulses is an emerging technique for SRF cavities. This technique can effectively reduce the cavities’ surface defects and improve their RF performance. However, previous studies in this field limited themselves on solid state lasers or gas lasers, which have very low average power and are not practical for processing actual SRF cavities with ~m2 inner surface area. IMP innovatively built a practical whole-cavity processing system with a kW-level nanosecond fiber laser, which is designed to process an SRF cavity with- in a working day. In this work, the system design and feasibility analysis will be given, together with the comparison between pristine Nb thin film samples on copper substrates and their fiber laser processed counterparts. The results show that our fiber laser system can deliver comparable surface treatment as that from the solid-state laser system, but with higher effi- ciency. The authors believe such results could boost the application of laser surface annealing technique in the particle accelerator community. SUPM038 Reinforcement Control and Matching for LEBT and RFQ of Linear Accelerators Chunguang Su , Zhijun Wang (Institute of Modern Physics, Chinese Academy of Sciences). Duanyang Jia, Xiaolong Chen, Xin Qi, Yongzhi Jia (Institute of Modern Physics, Chinese Academy of Sciences). As a scientific system with many subsystems, particle accelerator system is getting more com- plex， due to rising demands on accelerator performance. Meanwhile, it is increasingly difficult to study such complex systems using traditional research methods based on physical models. At present, machine learning (ML) is mature enough to be applied in accelerator science such as beam diagnostics and equipment control. Compared with traditional research methods, ma- chine learning has strong generality and high computational efficiency. However, problems such as incomplete database or insufficient test time often hinder the application of ML in ac- celerator operation control and optimization. To further explore the application of ML in accelerator science, in this paper, we demonstrate the feasibility of reinforcement learning in accelerator control using: 1) replacement model of linear accelerator components based on neural network; and 2) reinforcement control and fast matching of the LEBT and RFQ of the linear accelerator, which is based on reinforcement learn- ing. These methods will be experimentally verified on a linear accelerator. SUPM039 Design of the Gradient Dipole Magnet for LLICTF Yimeng Chu , Zhijun Wang (Institute of Modern Physics, Chinese Academy of Sciences). Chun Yan Jonathan Wong, Kunxiang sun, Weilong Chen, Zehua Liang, Zhenyu Xu (Institute of Modern Physics, Chinese Academy of Sciences).