论文标题
基于蒙特卡洛模拟和MLP神经网络的海洋放射性同位素伽马射线频谱分析方法
A marine radioisotope gamma-ray spectrum analysis method based on Monte Carlo simulation and MLP neural network
论文作者
论文摘要
使用闪烁探测器在海水中监测CS-137的依赖于谱分析方法来提取CS-137浓度。在统计不佳的情况下，传统净峰面积（NPA）方法的计算结果具有很大的不确定性。我们提出了一种基于机器学习的方法，以更好地分析CS-137浓度较低的伽马射线频谱。我们应用多层感知器（MLP）来分析海水光谱中CS-137的662 KEV全能峰。 MLP可以通过将模拟的CS-137信号与测量的背景频谱相结合，可以通过几个测量的背景谱系进行训练。因此，它可以节省准备和测量用于生成培训数据集的标准样本的时间。为了验证基于MLP的方法，我们使用通过海洋监测设备测量的Geant4和背景伽马射线频谱来生成独立的测试数据集，以通过我们的方法和传统的NPA方法来测试结果。我们发现，基于MLP的方法达到的均方根误差为0.159，比传统净峰面积方法低2.3倍，表明基于MLP的方法提高了CS-137浓度计算的精度
The monitoring of Cs-137 in seawater using scintillation detector relies on the spectrum analysis method to extract the Cs-137 concentration. And when in poor statistic situation, the calculation result of the traditional net peak area (NPA) method has a large uncertainty. We present a machine learning based method to better analyze the gamma-ray spectrum with low Cs-137 concentration. We apply multilayer perceptron (MLP) to analyze the 662 keV full energy peak of Cs-137 in the seawater spectrum. And the MLP can be trained with a few measured background spectrums by combining the simulated Cs-137 signal with measured background spectrums. Thus, it can save the time of preparing and measuring the standard samples for generating the training dataset. To validate the MLP-based method, we use Geant4 and background gamma-ray spectrums measured by a seaborne monitoring device to generate an independent test dataset to test the result by our method and the traditional NPA method. We find that the MLP-based method achieves a root mean squared error of 0.159, 2.3 times lower than that of the traditional net peak area method, indicating the MLP-based method improves the precision of Cs-137 concentration calculation
