The precise measurement of the antineutrino spectra produced by isotope fission in reactors is of great significance for studying neutrino oscillations, refining nuclear databases, and addressing the reactor antineutrino anomaly. In this paper, we report a method that utilizes a feedforward neural network (FNN) model to decompose the prompt energy spectrum observed in a short-baseline reactor neutrino experiment and extract the antineutrino spectra produced by the fission of major isotopes such as 235U, 238U, 239Pu, and 241Pu in the nuclear reactor. We present two training strategies for the model and compare them with the traditional χ2 minimization method by applying them to the same set of pseudo-data corresponding to a total exposure of (2.9×5×1800)GWth⋅tonnes⋅days. The results show that the FNN model not only converges faster and better during the fitting process but also achieves relative errors of less than 1\% in the 2−8 MeV range in the extracted spectra, outperforming the χ2 minimization method. The feasibility and superiority of this method were validated in the study.
@article{arxiv.2407.05834,
title = {Extraction of fissile isotope antineutrino spectra using feedforward neural network},
author = {Jian Chen and Jun Wang and Wei Wang and Yuehuan Wei},
journal= {arXiv preprint arXiv:2407.05834},
year = {2025}
}