Decomposition of fissile isotope antineutrino spectra using convolutional neural network

Subjects: Nuclear Science and Technology >> Engineering Technology of Fission Reactor submitted time 2023-06-01

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

Abstract： 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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