Extraction of fissile isotope antineutrino spectra using feedforward neural network

Precise measurement of 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. This work reports a method utilizing a feedforward neural network (FNN) model to decompose the reconstructed measured prompt energy spectrum observed by 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 a nuclear reactor. We present two training strategies for this model and compare them with the traditional χ² minimization method, analyzing the same set of pseudo-data for a total exposure of (2.9× 5× 1800)~GWₜₕ· tons· days. The results show that the FNN model not only converges faster and better during the fitting process but also achieves relative errors in the extracted spectra within 1% in the $2-8$ MeV range, outperforming the χ² minimization method. The feasibility and superiority of this method have been validated in this study.