30 August 2026 to 3 September 2026
Whistler Conference Centre
Canada/Pacific timezone
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An ultra-low latency ATCA-based data acquisition platform for real-time beam current monitoring and fast protection at CSNS-II

WERT05
2 Sept 2026, 11:40
20m
Rainbow Theatre (Whistler Conference Centre)

Rainbow Theatre

Whistler Conference Centre

4010 Whistler Way, Whistler, BC
Contributed Oral Presentation MC07: Data Acquisition and Processing Platforms MC07: Data Acquisition and Processing Platforms

Speaker

peng zhu (University of Science and Technology of China, National Synchrotron Radiation Laboratory, USTC, China Spallation Neutron Source, Institute of High Energy Physics, Chinese Academy of Sciences)

Description

The Phase-II upgrade of the China Spallation Neutron Source (CSNS-II) introduces a superconducting linear section, which imposes stringent machine protection requirements by necessitating a total system response time of less than 10 μs. This paper presents a high-throughput, ultra-low-latency data acquisition and processing platform specifically engineered for real-time beam status monitoring and the Fast Protection System. By bypassing standard bus protocol overheads, the architecture employs high-speed LVDS signaling (250 MHz on the backplane) and multi-gigabit serial links (up to 10 Gbps) to achieve deterministic, point-to-point data transmission between front-end modules and the master logic board. During the critical data acquisition process, an RFSoC-based digitizer utilizes 14-bit, 5 GSPS direct RF sampling to capture signals from the low-noise front-end electronics for Fast Current Transformer. To achieve the lowest possible latency, ardware-level DSP algorithms were implemented, including optimized IQ demodulation, pipelined CORDIC, and parallelized fault detection logic. This optimization constrains the processing latency to under 3 μs, enabling an end-to-end system response time of precisely 4.5 μs for superconducting cavity protection. This robust platform provides not only a reliable solution for bunch-by-bunch diagnostics but also a high-performance framework for future AI-driven predictive interlock and edge-computing applications in high-power accelerators.

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Author

peng zhu (University of Science and Technology of China, National Synchrotron Radiation Laboratory, USTC, China Spallation Neutron Source, Institute of High Energy Physics, Chinese Academy of Sciences)

Co-authors

Gongfa Liu (National Synchrotron Radiation Laboratory, USTC, University of Science and Technology of China) Renjun Yang (Institute of High Energy Physics) Sinong Cheng (Institute of High Energy Physics, Chinese Academy of Sciences) Weiling Huang (Institute of High Energy Physics) Xiaohan Lu (Institute of High Energy Physics) Yuliang Zhang (Institute of High Energy Physics)

Presentation materials

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