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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,部分核素的(γ,n)反应测量因来自不同实验室而分歧明显.本工作基于变分自编码器方法,针对原子核质量数在29到207区域的伽马单中子出射反应截面实验测量数据开展分析,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29Si、54Fe、63Cu、141Pr、181Ta、206Pb和207Pb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评价库(IAEA-2019-PD)评价值之间的偏差,检测变分自编码器的分析效果.经研究表明,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54Fe、63Cu、181Ta、206Pb和207Pb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 from different laboratories. Since experimental data analysis is the first tasks in nuclear data evaluation, identifying outlier data in measurements is crucial for improving the quality of nuclear data. Therefore, this work employs Variational AutoEncoder (VAE) approach to analyze experimental measurements of (γ, n) cross sections for nuclear mass from 29 to 207, aiming to provide more reliable experimental information for nuclear data evaluation.
Based on the proton Z and nuclear mass A, we constructed a Variational AutoEncoder network designed for outliers identification in measurement of (γ, n). The silhouette coeffcient method and K-Means algorithm were used to perform clustering the latent variables of VAE. Subsequently, the experimental data with and without the outliers were compared with the IAEA-2019-PD to assess VAE in application of photoneutron measurements evaluation.
The results demonstrate that VAE can effectively identify outliers in the measurements of (γ, n). After excluding outliers, the (γ, n) cross-section for 54Fe, 63Cu, 181Ta, 206Pb and 207Pb showed higher consistency with the IAEA-2019-PD evaluation results. However, 29Si and 141Pr deviated from the IAEA- 2019-PD evaluation results yet, which requires more analysis to the measurements itself in future.
The Variational AutoEncoder method effectively identifies outliers and mines the latent structures in experimental data of (γ, n) reaction. It provides more reliable experimental information for nuclear data evaluation and validating the potential application of this method in nuclear data research. However, generalization capability of Variational AutoEncoder still needs further developed especially the issues with uneven energy distribution for various measurements.-
Keywords:
- Variational Autoencoder /
- (γ,n) Reaction /
- Cross Section /
- Outlier
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