Author: Leng, Y.B.
Paper Title Page
MOCO01 Online Touschek Beam Lifetime Measurement Based on the Precise Bunch-By-Bunch Beam Charge Monitor 35
 
  • B. Gao, F.Z. Chen, Y.B. Leng
    SSRF, Shanghai, People’s Republic of China
  • Y.M. Zhou
    SINAP, Shanghai, People’s Republic of China
 
  Beam current and lifetime are the most important parameters to characterize the beam and machine quality of an electron storage ring. In order to describe the behavior of all electron bunches completely and accurately, a precisely bunch-by-bunch charge monitor has been developed at SSRF. Method called two-point equilibrium sampling is introduced to avoid the influence of longitudinal oscillation on the sampling point, thanks to this, the resolution of the BCM was below 0.2 pC. Utilizing the advantages of BCM’s high refresh rate and high resolution, the system can meet the requirement of monitor the bunch-by-bunch beam lifetime, measure touschek lifetime and vacuum lifetime. In this paper, experiments and and analysis will be described in detail.  
slides icon Slides MOCO01 [18.156 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IBIC2019-MOCO01  
About • paper received ※ 03 September 2019       paper accepted ※ 08 September 2019       issue date ※ 10 November 2019  
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TUPP004 High-Speed Beam Signal Processor for SHINE 278
 
  • L.W. Lai, Y.B. Leng
    SSRF, Shanghai, People’s Republic of China
 
  A CW hard X-ray FEL is under construction in SSRF, which pulse rate is designed to 1MHz. A new high-speed sampling BPM signal processor is under development to meet the high performance requirements of beam position measurement system. The processor’s sampling rate can be up to 500MHz, and beam position information of each bunch (1MHz rate) can be retrieved with the power of FPGA. Time stamp is aligned with the position data for offline analysis. The processor is designed to be a common signal processing platform for beam diagnostics. The first application is cavity BPM, and other applications, including button BPM, stripline BPM, and even wire scanner processor will be developed based on this platform. At the same time, a RF direct sampling processor is designed for cavity BPM signal processing. This novel technology will greatly simplify the cavity BPM electronic system, and make the system design more efficient and more flexible.  
poster icon Poster TUPP004 [0.983 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IBIC2019-TUPP004  
About • paper received ※ 04 September 2019       paper accepted ※ 07 September 2019       issue date ※ 10 November 2019  
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WEPP021 Machine Learning Image Processing Technology Application in Bunch Longitudinal Phase Data Information Extraction 561
 
  • X.Y. Xu, Y.M. Zhou
    SINAP, Shanghai, People’s Republic of China
  • Y.B. Leng, Y.M. Zhou
    SSRF, Shanghai, People’s Republic of China
  • X.Y. Xu
    University of Chinese Academy of Sciences, Beijing, People’s Republic of China
 
  To achieve the bunch-by-bunch longitudinal phase measurement, Shanghai Synchrotron Radiation Facility (SSRF) has developed a high resolution measurement system. We used this measurement system to study the injection transient process, and obtained the longitudinal phase of the refilled bunch and the longitudinal phase of the original stored bunch. A large number of parameters of the synchronous damping oscillation are included in this large amount of longitudinal phase data, which are important for the evaluation of machine state and bunch stability. The multi-turn phase data of a multi-bunch is a large two-dimensional array that can be converted into an image. The convolutional neural network (CNN) is a machine learning model with strong capabilities in image processing. We hope to use the convolutional neural network to process the longitudinal phase two-dimensional array data, and extract important parameters such as the oscillation amplitude and the synchrotron damping time.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IBIC2019-WEPP021  
About • paper received ※ 23 August 2019       paper accepted ※ 10 September 2019       issue date ※ 10 November 2019  
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