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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Data Availability
The data that support the findings of this study are openly available upon request in Science Data Bank at https://doi.org/10.57760/sciencedb.j00186.00075 and https://cstr.cn/31253.11.sciencedb.j00186.00075
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Acknowledgements
The authors would like to thank Prof. Zhi-Bing Li (School of Physics, Sun Yat-sen University) for useful discussion during the course of this research.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yu-Da Zeng, guided by Feng-Peng An and Wei Wang. The first draft of the manuscript was written by Yu-Da Zeng, Feng-Peng An and Wei Wang, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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This work was supported by the National Natural Science Foundation of China (Nos. 11675273 and 12075087) and the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA10011102).
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Zeng, YD., Wang, J., Zhao, R. et al. Decomposition of fissile isotope antineutrino spectra using convolutional neural network. NUCL SCI TECH 34, 79 (2023). https://doi.org/10.1007/s41365-023-01229-9
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DOI: https://doi.org/10.1007/s41365-023-01229-9