Machine Learning Based Compton Suppression for Nuclear Fusion Plasma Diagnostics

IF 1.9 4区 工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY Journal of Fusion Energy Pub Date : 2024-05-21 DOI:10.1007/s10894-024-00408-9
Kimberley Lennon, Chantal Shand, Robin Smith
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Abstract

Diagnostics are critical on the path to commercial fusion reactors, since measurements and characterisation of the plasma is important for sustaining fusion reactions. Gamma spectroscopy is commonly used to provide information about the neutron energy spectrum from activation analysis, which can be used to calculate the neutron flux and fusion power. The detection limits for measuring nuclear dosimetry reactions used in such diagnostics are fundamentally related to Compton scattering events making up a background continuum in measured spectra. This background lies in the same energy region as peaks from low-energy gamma rays, leading to detection and characterisation limitations. This paper presents a digital machine learning Compton suppression algorithm (MLCSA), that uses state-of-the-art machine learning techniques to perform pulse shape discrimination for high purity germanium (HPGe) detectors. The MLCSA identifies key features of individual pulses to differentiate between those that are generated from photopeaks and Compton scatter events. Compton events are then rejected, reducing the low energy background. This novel suppression algorithm improves gamma spectroscopy results by lowering minimum detectable activity (MDA) limits and thus reducing the measurement time required to reach the desired detection limit. In this paper, the performance of the MLCSA is demonstrated using an HPGe detector, with a gamma spectrum containing americium-241 (Am-241) and cobalt-60 (Co-60). The MDA of Am-241 improved by 51% and the signal to background ratio improved by 49%, while the Co-60 peaks were partially preserved (reduced by 78%). The MLCSA requires no modelling of the specific detector and so has the potential to be detector agnostic, meaning the technique could be applied to a variety of detector types and applications.

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基于机器学习的核聚变等离子体诊断康普顿抑制技术
在通往商用聚变反应堆的道路上,诊断技术至关重要,因为等离子体的测量和特征描述对于维持聚变反应非常重要。伽马能谱通常用于提供活化分析得出的中子能谱信息,可用于计算中子通量和聚变功率。此类诊断中使用的核剂量测定反应的探测极限与康普顿散射事件有关,康普顿散射事件构成了测量光谱中的背景连续体。这种背景与低能量伽马射线的峰值处于同一能量区域,从而导致了探测和特征描述的局限性。本文介绍了一种数字机器学习康普顿抑制算法(MLCSA),它采用最先进的机器学习技术,对高纯锗(HPGe)探测器进行脉冲形状判别。MLCSA 能识别单个脉冲的关键特征,以区分光峰和康普顿散射事件产生的脉冲。然后剔除康普顿事件,减少低能量背景。这种新颖的抑制算法通过降低最小可探测活度(MDA)限值来改进伽马能谱分析结果,从而缩短达到所需探测限值所需的测量时间。本文使用 HPGe 探测器演示了 MLCSA 的性能,伽马能谱包含镅-241(Am-241)和钴-60(Co-60)。Am-241 的 MDA 提高了 51%,信噪比提高了 49%,而 Co-60 的峰值得到了部分保留(减少了 78%)。MLCSA 不需要对特定探测器进行建模,因此具有与探测器无关的潜力,这意味着该技术可应用于各种探测器类型和应用。
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来源期刊
Journal of Fusion Energy
Journal of Fusion Energy 工程技术-核科学技术
CiteScore
2.20
自引率
0.00%
发文量
24
审稿时长
2.3 months
期刊介绍: The Journal of Fusion Energy features original research contributions and review papers examining and the development and enhancing the knowledge base of thermonuclear fusion as a potential power source. It is designed to serve as a journal of record for the publication of original research results in fundamental and applied physics, applied science and technological development. The journal publishes qualified papers based on peer reviews. This journal also provides a forum for discussing broader policies and strategies that have played, and will continue to play, a crucial role in fusion programs. In keeping with this theme, readers will find articles covering an array of important matters concerning strategy and program direction.
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