摘要
人工神经网络由于其优良的自我调节能力及学习能力,已经被广泛地应用在各领域的非线性分析中.在中国锦屏极深地下实验室(CJPL)中的低本底液闪中子探测器一直在记录着中子的本底数据,探测器输出的能谱实际上是核反冲能谱,与输入能谱可一一对应,并随着输入能谱的改变而发生改变;因此可以将探测器输出信号输入到训练过的神经网络中判断输入能谱.本论文采用的神经网络为Elman神经网络,训练神经网络采用的数据为Geant4模拟所得.将实验获取的核反冲能谱输入到训练过的神经网路进行反解,最后Elman网络反解出的Am-Be中子源能谱与真实谱误差在0.1%~11.8%,反解出的252Cf中子源能谱与真实谱误差在0.1%~8.9%.
Artificial neural networks have been widely used in nonlinear analysis in various fields due to their excellent self-regulation and learning ability. Low background liquid scintillator neutron detector in China Jinping underground laboratory(CJPL) have been recording neutron background data. The energy spectrum of detector output is actually the nuclear recoil energy spectrum, which can be in one-to-one correspondence with the input spectrum, and changes as the parameters of the input change. Therefore, the detector output signal can be input into the trained neural network to determine the emission spectrum of the external radiation source. The neural network used in this paper is the Elman neural network, and the data used in the training neural network is simulated by Geant4. The nuclear recoil energy spectrum obtained from the experiment is input into the trained neural network for decomposition. Finally, the Elman network has a spectral error of 0.1%~11.8% for the Am-Be neutron source and 0.1%~8.9% for the 252Cf neutron source.
作者
莫双荣
刘钰
幸浩洋
朱敬军
张乐
王桢
MO Shuang-Rong;LIU Yu;XING Hao-Yang;ZHU Jing-Jun;ZHANG Le;WANG Zhen(Key Laboratory of Radiation Physics and Technology of Ministry of Education,Institute of Nuclear Science and Technology,Sichuan University,Chengdu 610064,China;College of Physics,Sichuan University,Chengdu 610064,China)
出处
《四川大学学报（自然科学版）》
CAS
CSCD
北大核心
2020年第3期531-534,共4页
Journal of Sichuan University(Natural Science Edition)
基金
国家自然科学基金面上项目(11275134)。
