Decomposition of fissile isotope antineutrino spectra using convolutional neural network

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 p...

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Bibliographic Details
Published in:Nuclear science and techniques 2023-05, Vol.34 (5), p.182-190, Article 79
Main Authors: Zeng, Yu-Da, Wang, Jun, Zhao, Rong, An, Feng-Peng, Xiao, Xiang, Hor, Yuenkeung, Wang, Wei
Format: Article
Language:English
Subjects:
Beam Physics
Nuclear Energy
Particle Acceleration and Detection
Particle and Nuclear Physics
Physics
Physics and Astronomy
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Summary: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.
ISSN:1001-8042
2210-3147
DOI:10.1007/s41365-023-01229-9