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Description
Real-time bunch-by-bunch monitoring of transverse position and longitudinal phase has become increasingly important for the stable operation of storage-ring light sources and for accelerator physics studies. This paper presents a real-time 3D bunch-by-bunch position measurement system based on machine learning. The system first employs high-speed ADCs to perform RF direct sampling on the BPM electrode signals. By deploying neural network models within the system, the system simultaneously achieves real-time measurement of the transverse position, longitudinal phase, and charge of each bunch. The system was tested at Shanghai Synchrotron Radiation Facility (SSRF), with results demonstrating satisfactory resolution and low latency. The system also maintains consistent performance under different machine conditions, meeting the real-time requirements for bunch-by-bunch diagnostics in storage rings.
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