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伽马单中子出射反应截面是核工程输运计算中的重要参数, 部分核素(γ, n)的反应测量因来自不同实验室而分歧明显. 本文基于变分自编码器方法, 针对原子核质量数在29—207区域的伽马单中子出射反应截面实验测量数据进行分析, 有效识别多家测量之间的离群点. 首先, 研究变分自编码器方法, 建立伽马单中子光核测量数据离群点识别网络; 其次, 对^29\textSi, ^54\textFe, ^63\textCu, ^141\textPr, ^181\textTa, ^206\textPb和^207\textPb的29家多能点测量数据进行离群点识别; 最后, 计算离群点识别前后的实验数据与国际原子能机构光核评价数据库(IAEA-2019-PD)评价值之间的偏差, 检测变分自编码器的分析效果. 研究表明, 变分自编码器方法可以有效识别(γ, n)反应实验测量离群点, 其中^54\textFe,^63\textCu, ^181\textTa, ^206\textPb和^207\textPb的伽马单中子出射反应截面与IAEA-2019-PD评价结果一致性更高, 验证了该方法在核数据研究中的应用潜力.The (γ, n) cross-section is important in nuclear engineering transport calculations. The measurements of the (γ, n) reaction for some isotopes show significant discrepancies among different laboratories. Since the analysis of experimental data is the primary task in the evaluation of nuclear data, identifying the measured outlier data is crucial for improving the quality of nuclear data. Therefore, a variational autoencoder (VAE) method is used in this work to analyze experimental measurements of (γ, n) cross sections for nuclear mass ranging from 29 to 207, in order to provide more reliable experimental information for evaluating nuclear data.According to the proton number Z and nuclear mass A, we design a variational autoencoder network for outlier identification in the measurement of (γ, n). The silhouette coefficient method and K-means algorithm are used to cluster the latent variables of VAE. Subsequently, the experimental data with and without the outliers are compared with those from the IAEA-2019-PD to assess the VAE in its application to the evaluation of photoneutron measurements.The results demonstrate that the VAE can effectively identify outliers in the measurements of (γ, n). After excluding outliers, the (γ, n) cross-section for ^54\textFe, ^63\textCu, ^181\textTa, ^206\textPb, and ^207\textPb showed higher consistency with the IAEA-2019-PD evaluation results. However, ^29\textSi and ^141\textPr still deviate from the IAEA-2019-PD evaluation results, therefore requiring more analyses of the measurements themselves in future.The VAE method can effectively identify outliers and extract the latent structures in experimental data of (γ, n) reaction. It provides more reliable experimental information for evaluating nuclear data and validating the potential application of this method in nuclear data research. However, the generalizability of VAE method still needs further developing, especially in addressing the issues of uneven energy distribution for various measurements.
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Keywords:
- variational autoencoder /
- (γ, n) reaction /
- cross section /
- outlier
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中国物理学会期刊
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