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Neutron spectrum unfolding using artificial neural network and modified least square method

机译:人工神经网络和改进最小二乘法的中子谱展开

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In the present paper, neutron spectrum is reconstructed using the Artificial Neural Network (ANN) and Modified Least Square (MLSQR) methods. The detector's response (pulse height distribution) as a required data for unfolding of energy spectrum is calculated using the developed MCNPX-ESUT computational code (MCNPX-Energy engineering of Sharif University of Technology). Unlike the usual methods that apply inversion procedures to unfold the energy spectrum from the Fredholm integral equation, the MLSQR method uses the direct procedure. Since liquid organic scintillators like NE-213 are well suited and routinely used for spectrometry of neutron sources, the neutron pulse height distribution is simulated/measured in the NE-213 detector. The response matrix is calculated using the MCNPX-ESUT computational code through the simulation of NE-213 detector's response to monoenergetic neutron sources. For known neutron pulse height distribution, the energy spectrum of the neutron source is unfolded using the MLSQR method. In the developed multilayer perception neural network for reconstruction of the energy spectrum of the neutron source, there is no need for formation of the response matrix. The multilayer perception neural network is developed based on logsig, tansig and purelin transfer functions. The developed artificial neural network consists of two hidden layers of type hyperbolic tangent sigmoid transfer function and a linear transfer function in the output layer. The motivation of applying the ANN method may be explained by the fact that no matrix inversion is needed for energy spectrum unfolding. The simulated neutron pulse height distributions in each light bin due to randomly generated neutron spectrum are considered as the input data of ANN. Also, the randomly generated energy spectra are considered as the output data of the ANN. Energy spectrum of the neutron source is identified with high accuracy using both MLSQR and ANN methods. The results obtained from MLSQR and ANN methods for Cf-252 and Am-241-Be-9 source are validated against the ISO spectrum. The unfolded neutron energy spectra from both MLSQR and ANN methods show a good agreement with the actual spectrum of Cf-252 and Am-241-Be-9 source. (C) 2016 Elsevier Ltd. All rights reserved.
机译:在本文中,中子谱是使用人工神经网络(ANN)和改进的最小二乘(MLSQR)方法重建的。使用开发的MCNPX-ESUT计算代码(谢里夫工业大学的MCNPX-能源工程)计算检测器的响应(脉冲高度分布),作为能谱展开所需的数据。与应用反演程序以从Fredholm积分方程展开能谱的常规方法不同,MLSQR方法使用直接程序。由于像NE-213这样的液态有机闪烁体非常适合并常规用于中子源光谱测定,因此在NE-213检测器中模拟/测量了中子脉冲高度分布。通过模拟NE-213检测器对单能中子源的响应,使用MCNPX-ESUT计算代码来计算响应矩阵。对于已知的中子脉冲高度分布,使用MLSQR方法展开中子源的能谱。在用于重建中子源能谱的发达的多层感知神经网络中,不需要形成响应矩阵。多层感知神经网络是基于logsig,tansig和purelin传递函数开发的。发达的人工神经网络由两个双曲线正切S型传递函数和输出层中的线性传递函数的两个隐藏层组成。可以通过以下事实解释应用ANN方法的动机:能量谱展开不需要矩阵求逆。将由于随机生成的中子谱而在每个光仓中模拟的中子脉冲高度分布视为ANN的输入数据。而且,随机产生的能谱被认为是人工神经网络的输出数据。使用MLSQR和ANN方法可以高精度地识别中子源的能谱。通过CLS-252和Am-241-Be-9来源的MLSQR和ANN方法获得的结果已针对ISO光谱进行了验证。来自MLSQR和ANN方法的未折叠中子能谱显示出与Cf-252和Am-241-Be-9源的实际光谱有很好的一致性。 (C)2016 Elsevier Ltd.保留所有权利。

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