Extraction of fissile isotope antineutrino spectra using feedforward neural network

TL;DR

This paper introduces a neural network-based method for accurately extracting isotope-specific antineutrino spectra from reactor data, outperforming traditional fitting techniques in speed and precision.

Contribution

The study presents a novel feedforward neural network approach with two training strategies for isotope spectrum extraction, demonstrating superior accuracy and efficiency over traditional methods.

Findings

Achieved less than 1% relative error in spectrum extraction

FNN converges faster and more accurately than $$ minimization

Validated the method's feasibility and superiority

Abstract

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 $^{235}$U, $^{238}$U, $^{239}$Pu, and $^{241}$Pu in the nuclear reactor. We present two training strategies for the model and compare them with the traditional $\chi^2$ minimization method by applying them to the same set of pseudo-data corresponding to a total exposure of $(2.9\times 5\times 1800)~\rm{GW_{th}\cdot tonnes\cdot days}$. 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 $\chi^2$ minimization method. The feasibility and superiority of this method were validated in the study.