A Linear Recurrences Based Pseudo-Random Number Generator Optimized for Detector Emulators
Digital Detector Emulators (DDEs) are important tools for developing particle flux monitoring systems in the laboratory. However, as the dynamic range of modern particle flux monitoring systems, particularly neutron flux monitoring systems, continues to rapidly increase, DDEs must also increase thei...
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Veröffentlicht in: | IEEE transactions on nuclear science 2023-06, p.1-1 |
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Sprache: | eng |
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Zusammenfassung: | Digital Detector Emulators (DDEs) are important tools for developing particle flux monitoring systems in the laboratory. However, as the dynamic range of modern particle flux monitoring systems, particularly neutron flux monitoring systems, continues to rapidly increase, DDEs must also increase their maximum count rate to keep up. The pulse array generating module is the core of the DDE, which utilizes pseudo-random numbers generated by linear-feedback shift registers (LFSRs) to control the intensity and amount of simulated neutrons emitted in each clock cycle. However, the defects or limitations associated with pseudo-random number generators (PRNGs) can cause deviations from the ideal situation, which affects the quality of the output waveform. We propose a novel design that achieves optimal quality of simulated pulse signals with the same computational cost compared to existing methods, along with a customized algorithm based on the property of linear transformation that can automatically generate the desired LFSR structure. We evaluate simulated results of the PRNG and pulse array generator from multiple aspects to avoid any systemic errors in the design. In addition, we collect and analyze measured Campbell value data of the digital detector emulator. This study aims to contribute to the development of more accurate particle flux monitoring systems by improving the capabilities and reliability of DDEs, addressing the issue of PRNGs, and proposing a novel design that achieves optimal quality of simulated pulse signals. |
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ISSN: | 0018-9499 |
DOI: | 10.1109/TNS.2023.3285251 |