Decomposition of fissile isotope antineutrino spectra using convolutional neural network

分类： 核科学技术 >> 裂变堆工程技术 提交时间： 2023-06-01

Zeng, Yuda Wang, Jun Zhao, Rong An, Fengpeng Xiao, Xiang Hor, Yuenkeung Wang, Wei

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.

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