arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1509.06259physics.ins-dethep-ex
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基于Elman神经网络的液体scintillator中子和γ射线鉴别

Discrimination of neutrons and γ-rays in liquid scintillator based on Elman neural network

Cai-Xun Zhang, Shin-Ted Lin, Jian-Ling Zhao, Li Wang, Xun-Zhen Yu, Jing-Jun Zhu, Hao-Yang Xing

摘要:

为了提高液体闪烁体(LS)探测器的中子和γ(n/γ)分辨性能,提出了一种基于Elman神经网络(ENN)的中子和γ分辨方法。中子和γ数据是由EJ-335 LS探测器在241 Am-9 Be辐射场中获得的。用两种人工神经网络方法区分中子和γ事件,包括ENN和一个典型的反向传播神经网络(BPNN)作为控制。结果表明,两种方法具有不同的n/γ鉴别性能。与BPNN相比,ENN在n/γ分辨方面提供了改进的品质因数(FOM)。通过使用ENN的新方法,FOM从0.907 { \pm } 0.034增加到0.953 { \pm } 0.037。提出的基于ENN的n/γ鉴别方法为中子探测中脉冲波形鉴别提供了一种新的选择。

英文摘要:

In this work, a new neutron and γ(n/γ) discrimination method based on an Elman Neural Network (ENN) is proposed to improve the discrimination performance of liquid scintillator (LS) detectors. Neutron and γ data were acquired from an EJ-335 LS detector, which was exposed in a 241Am-9Be radiation field. Neutron and γ events were discriminated using two methods of artificial neural network including the ENN and a typical Back Propagation Neural Network (BPNN) as a control. The results show that the two methods have different n/γ discrimination performances. Compared to the BPNN, the ENN provides an improved of Figure of Merit (FOM) in n/γ discrimination. The FOM increases from 0.907 {\pm} 0.034 to 0.953 {\pm} 0.037 by using the new method of the ENN. The proposed n/γ discrimination method based on ENN provides a new choice of pulse shape discrimination in neutron detection.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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