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TUOA02 Application of Machine Learning to Beam Diagnostics optics, diagnostics, controls, simulation 169
 
  • E. Fol, J.M. Coello de Portugal, R. Tomás
    CERN, Geneva, Switzerland
 
  Machine learning techniques are used in various scientific and industry fields as a powerful tool for data analysis and automatization. The presentation is devoted to exploration of relevant machine learning methods for beam diagnostics. The target is to provide an insight into modern machine learning techniques, which can be applied to improve current beam diagnostics and general applications in accelerators. Possible concepts for future applications are also presented.  
slides icon Slides TUOA02 [2.497 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IBIC2018-TUOA02  
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WEPC15 Machine Learning Applied to Predict Transverse Oscillation at SSRF diagnostics, SRF, injection, storage-ring 512
 
  • B. Gao, J. Chen, Y.B. Leng, Y.M. Zhou
    SINAP, Shanghai, People's Republic of China
 
  A fast beam size diagnostic system has been developed at SSRF (Shanghai Synchrotron Radiation Facility) storage ring for turn-by-turn and bunch-by-bunch beam transverse oscillation study. This system is based on visible synchrotron radiation direct imaging system. Currently, this system already has good experimental results. However, this system still has some limitations, the resolution is subject to the point spread function and the speed of online data processing is limited by the complex algorithm. We present a technique that applied machine learning tools to predict transverse oscillation.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IBIC2018-WEPC15  
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