Gamma spectrum analysis method for CLYC detectors based on Monte Carlo-simulated energy response
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摘要: 对于能量分辨能力不足的探测器，能谱解析工作能够提高核素识别和活度计算的准确度。CLYC探测器以其能够同时探测中子和γ光子的优点被广泛应用于中子-光子双模探测领域中，其能量分辨能力与高纯锗、碲锌镉等半导体探测器相比相对较差，在复杂的辐射环境中难以保证对γ能谱的分析精度。采用蒙特卡罗方法计算CLYC探测器的γ能量响应函数，并通过插值法构建探测器的能量响应矩阵，利用极大似然期望最大化算法（MLEM）进行γ能谱解析。选取0～3 MeV的能量区间，每间隔0.05 MeV计算一个响应函数，利用插值算法构建了CLYC探测器对γ射线的高精度响应矩阵，并结合MLEM算法对226Ra能谱、60Co - 137Cs混合能谱以及152Eu复杂能谱进行解谱验证，对特征峰面积进行了定量计算。结果表明：该方法能够有效克服探测器能量分辨率的限制，解谱后特征峰位清晰，复杂能谱中的重峰区域实现了有效分离，特征峰面积计算结果稳定，清晰反映了入射γ射线的能量和强度信息，提高了能谱分析的精度。Abstract:
Background Precise γ-ray spectrum analysis is essential for nuclide identification and activity quantification, but faces significant challenges when using low-resolution detectors such as CLYC scintillators in complex radiation fields. The limited energy resolution of these detectors often leads to overlapping peaks and obscured characteristic spectral features, which complicates accurate spectrum interpretation.Purpose This study aims to overcome the inherent energy resolution limitations of CLYC detectors by developing a spectrum deconvolution method that can recover clear spectral information and separate overlapping peaks in complex γ-ray spectra.Methods A detector energy response matrix was constructed by combining Monte Carlo simulations to calculate γ-ray energy response functions with an interpolation method. Response functions were derived across the 0～3 MeV energy range at intervals of 0.05 MeV to ensure high precision. Spectrum deconvolution was then performed using the Maximum Likelihood Expectation Maximization (MLEM) algorithm, which was then applied to analyze the original complex spectrum.Results The method was validated by unfolding the spectra of a 226Ra source, a mixed 60Co - 137Cs source, and the complex spectrum of 152Eu. The unfolded spectrum exhibited well-resolved characteristic peaks, effective separation of severely overlapping spectral regions, and stable quantitative results for characteristic peak areas.Conclusions The proposed approach significantly enhances the precision of γ-ray spectrum analysis with CLYC detectors. It successfully reveals the energy and intensity information of incident γ-rays, mitigates the detector’s resolution limitations, and provides a reliable method for analyzing spectrum in complex radiation environment.-
Key words:
- spectrum unfolding /
- MLEM algorithm /
- CLYC detector /
- response function
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图 1 两个模拟响应函数之间的插值方法
Figure 1. Interpolation method between two simulated response functions
图 4 60Co - 137Cs混合源及226Ra能谱图
Figure 4. Gamma-ray spectrum of 60Co - 137Cs mixed source and 226Ra source
图 7 CLYC探测器对γ射线的响应矩阵R
Figure 7. Energy response matrix of CLYC detectors to gamma rays
图 8 不同迭代次数下的解谱对比
Figure 8. Comparison of spectrum unfolding results under different iteration counts
图 9 60Co - 137Cs混合源
3000 次迭代解谱对比Figure 9. Spectrum unfolding at
3000 iterations of 60Co - 137Cs mixed source
图 11 特征峰面积随迭代次数的变化曲线
Figure 11. Variation of characteristic peak area with the number of iterations
表 1 解谱结果
Table 1. Results of energy spectrum interpretation
energy/
MeVexperimental
FWHM/MeVanalytical
FWHM/MeVexperimental
channelanalytical
channelexperimental
resolution/%analytical
resolution/%relative
improvement/%0.196 0.02387 0.00581 66 65 12.17 2.97 75.60 0.295 0.02855 0.01565 93 95 9.68 5.31 45.18 0.386 0.03912 0.00965 124 122 10.13 2.50 75.33 0.647 0.06931 0.01098 213 212 10.71 1.70 83.25 0.662 0.07012 0.01525 223 224 10.59 2.30 78.25 1.173 0.07939 0.05184 390 390 6.77 4.42 34.70 1.332 0.08402 0.05149 446 445 6.31 3.87 38.72 
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表 2 不同迭代次数下的峰面积计算结果
Table 2. Calculation results of peak areas at different numbers of iterations
iterations energy/MeV 0.196 0.295 0.386 0.647 0.662 1.173 1.332 50 14056 / 34385 26088 128649 473261 478623 100 13397 11399 24484 25572 143790 479086 493475 500 12312 13905 23123 25062 146766 476543 508245 1000 12227 14195 23157 24976 145330 478110 506116 2000 12128 14052 23225 24891 144097 480865 497697 3000 12114 14005 23282 24857 143585 491375 490585 4000 12103 13982 23314 24835 143322 491351 491791
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