Subjects: Nuclear Science and Technology >> Nuclear Science and Technology submitted time 2024-02-04
Abstract： CAFE-2 is a superconducting linear accelerator built by the Institute of Modern Physics of the Chinese Academy of Sciences (IMP) in 2021 to carry out research on new superheavy nuclides synthesis, chemical properties of superheavy elements, and the structure and properties of superheavy nuclei. The synchronized timing trigger system is an important subsystem of CAFE-2 and plays a key role in the operation of CAFE-2. The CAFE-2 main synchronization timing system adopts the classical event-based timing system. At the same time, in order to meet the needs of different operation timing and synchronization triggering of CAFE-2 in both accelerator commissioning and terminal operation modes, the synchronization trigger signal of the rotating target modulation is coupled with the main timing CW(Continuous-wave) trigger signal in the terminal operation mode through the design of the coupling circuit and interlocking logic. The problem of different trigger signal sources for CAFE-2 in accelerator commissioning and terminal operation modes is solved. This paper introduces the structure of CAFE-2 synchronous timing trigger system, and describes the principle and scheme of CAFE-2 synchronous timing trigger system in different operation modes.
Subjects: Nuclear Science and Technology >> Particle Accelerator Subjects: Electronics and Communication Technology >> Electron Technology submitted time 2023-11-29
Abstract： In light of the current international energy scarcity, nuclear power has emerged as a crucial source of clean energy. Proton accelerators have therefore become a pivotal technology in nuclear waste management. During beamline orbit correction processes, precise calculations of beamline orbit parameters are required. Given the demonstrated effectiveness of neural networks in a wide variety of industry domains, they offer promising potential for high-accuracy data fitting and prediction. Hence, this study proposes a novel direct linear accelerator beamline orbit parameter prediction technique based on edge intelligence computing nodes. This technique leverages BPNN to learn from historical data and generate a powerful model that can be seamlessly deployed to edge computing nodes, thereby accelerating the prediction of BPM location parameters. Furthermore, the proposed approach may be complemented by an adaptive compensation system in the future, which, in combination with edge computing nodes, could enable automatic beamline position correction, thereby achieving beamline orbit correction. Our experimental results demonstrate that FPGA, as an edge acceleration node, can achieve an inference speed of 2.5us, which represents a remarkable performance enhancement of approximately 165.6 times compared to CPU and approximately 7.9 times compared to GPU. The predicted results exhibit an average error of only 0.5%, and they exhibit the desired latency and accuracy characteristics.
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