Beam Instrumentation System for Shanghai Soft X-ray FEL Test Facility
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L.W. Lai, F.Z. Chen, J. Chen, J. Chen, W. Fang, C. Feng, B. Gao, R. Jiang, Y.B. Leng, Y.B. Yan, L.Y. Yu, R.X. Yuan, N. Zhang, W.M. Zhou, T. Shen
SSRF, Shanghai, People’s Republic of China
S.S. Cao, L.F. Hua
SINAP, Shanghai, People’s Republic of China
Shanghai Soft X-ray FEL (SXFEL) test facility was designed and built to demonstrate EEHG and HGHG schemes and verify key technologies for the future hard X-ray FEL facility (SHINE). After three years commissioning 8.8 nm FEL radiation with peak power of 1 MW had been achieved at the end of 2019. The design, fabrication, commissioning and operation of BI system including Stripline-BPM, Cavity-BPM, screen monitor, bunch length monitor, beam arrival monitor, bunch energy monitor, will be introduced in this paper. Several lessons learned during design stage and beam commissioning stage will be addressed as well.
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J. Chen, S.S. Cao, F.Z. Chen, B. Gao, L.W. Lai, Y.B. Leng, T. Wu, X.Y. Xu, R.X. Yuan, Y.M. Zhou
SSRF, Shanghai, People’s Republic of China
Precise bunch charge measurement is the fundamental of charge feedback, beam lifetime measurement, beam loss monitor, as well as the basis of the related interlocking work. Beam position monitor (BPM) is often used for high-precision bunch charge measurement due to its superior performance. In this paper, the pros and cons of different types of BPM for measurement of bunch charge in storage ring and free electron laser (FEL) will be discussed. The related simulations, beam experiment and signal processing methods are also mentioned. The beam experiments results show that the relative bunch charge resolution of the Button BPM can reach 0.02％ in SSRF, 0.073％ and 0.021％ of the SBPM and CBPM in SXFEL, respectively. Besides, based on the method of beam experiments, we systematically studied the position dependence of BPM pickup and related compensation algorithms for high-precision bunch charge measurement.
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Prototype Design of Bunch Arrival Time Measurement System Based on Cavity Monitor for SHINE
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Y.M. Zhou, S.S. Cao, J. Chen, Y.B. Leng
SSRF, Shanghai, People’s Republic of China
The Shanghai high repetition rate XFEL and extreme light facility (SHINE) is planned to be built into one of the most efficient and advanced free-electron laser user facilities over the world to provide a unique tool for kinds of cutting-edge scientific research. The measurement of bunch arrival time is one of the key issues to optimize system performance. This is because the FEL facility relies on the synchronization of electron bunch and seeded lasers. Currently, there are mainly two methods to measure the bunch arrival time: the electro-optical sampling method and the RF cavity-based method. Considering the latter one has a simpler system and lower cost, the method has been adopted by SXFEL. The previous results show that the measurement uncertainty of bunch arrival time has achieved to be 45 fs, which can be further optimized. For SHINE, the bunch arrival time resolution is required to be better than 25 fs@100pC, and 200 fs@10 pC. The RF cavity-based method will also be applied. This paper will present the system prototype design and related simulation results.
High-Accuracy Diagnostic Tool for Beam Position Monitor Troubleshooting in SSRF Based on Clustering Analysis
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R. Jiang, J. Chen, Y.B. Leng
SSRF, Shanghai, People’s Republic of China
Beam position monitors (BPMs) are important to monitor the beam moving steadily. In spite of some data is viewed and analysed, a large fraction of data has never been effectively analysed in accelerator operation. It lead to some useful information not coming to the surface during the beam position monitor troubleshooting processing. We will describe in this paper our efforts to use clustering analysis techniques to pull out new information from existing beam data. Our focus has been to look at malfunction of BPM, associating basic running data that is ß oscillation of X and Y directions, energy oscillation and doing predictive analysis. Clustering analysis results showed that 140 BPMs could be classify into normal group and fault group and abnormal BPM could be separated. Based on the results, the algorithm could locate fault BPM and it could be an effective supplement for data analysis in accelerator physics.
