Decomposition of fissile isotope antineutrino spectra using convolutional neural network  

作　　者：Yu-Da Zeng Jun Wang Rong Zhao Feng-Peng An Xiang Xiao Yuenkeung Hor Wei Wang 

机构地区：[1]School of Physics,Sun Yat-sen University,Guangzhou,510275,China [2]Sino-French Institute of Nuclear Engineering and Technology,Sun Yat-sen University,Zhuhai,519082,China

出　　处：《Nuclear Science and Techniques》2023年第5期183-191,共9页核技术（英文）

基　　金：supported by the National Natural Science Foundation of China (Nos.11675273 and 12075087);the Strategic Priority Research Program of the Chinese Academy of Sciences (No.XDA10011102)。

摘　　要：Recent reactor antineutrino experiments have observed that the neutrino spectrum changes with the reactor core evolution and that the individual fissile isotope antineutrino spectra can be decomposed from the evolving data,providing valuable information for the reactor model and data inconsistent problems.We propose a machine learning method by building a convolutional neural network based on a virtual experiment with a typical short-baseline reactor antineutrino experiment configuration:by utilizing the reactor evolution information,the major fissile isotope spectra are correctly extracted,and the uncertainties are evaluated using the Monte Carlo method.Validation tests show that the method is unbiased and introduces tiny extra uncertainties.

关 键 词：Reactor antineutrino Isotope antineutrino spectrum decomposition Convolutional neural network 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] O572.321[自动化与计算机技术—控制科学与工程]