小尺度参考辐射场中散射γ能谱的计量特征提取方法

Metrological Feature Extraction Method for Scattering γ Spectrum in Minitype Reference Radiation

在小尺度参考辐射(MRR)场γ射线空气比释动能约定真值(CAK)的确定方法中,散射γ能谱由于受放射性统计涨落与测量时电子学噪声的干扰及存在的数据冗余问题而不能准确地表征MRR的特征信息,影响了CAK预测模型的构建效率与预测精度。本文采用了小波分析与主元分析(PCA)方法依次对散射γ能谱进行降噪处理和计量特征提取。所提取的计量特征用于替代原始能谱数据并构建了CAK的预测模型,其构建效率与预测精度均得到明显提高。

In the determination of the gamma air kerma conventional true value （CAK ） in a minitype reference radiation （MRR） ,the scattering γ spectrum is affected by the radioactive statistical fluctuation and other electronic noise .Moreover ,the excess data of the spectrum after dispersing also make it unable to characterize the MRR accurately and decrease the construction efficiency and prediction precision of the prediction model of CAK .The wavelet analysis and principal component analysis （PCA） were employed to de‐noise the scattering γ spectrum and extract the metrological feature components . When the metrological feature components were used to replace the spectrum for CAK prediction model construction ,the construction efficiency and prediction precision were＆amp;nbsp;improved significantly .