Neural network predictions of inclusive electron-nucleus cross sections

Abstract

We investigate whether a neural network approach can reproduce and predict the electron-nucleus cross sections in the kinematical domain of present and future accelerator-based neutrino oscillation experiments. For this purpose, we consider the large amount of data available to the community via the University of Virginia's Quasielastic Electron Nucleus Scattering Archive and use a residual, fully connected feedforward neural network. We illustrate the training performances of the neural network by comparing its results with experimental data for the electron double-differential cross section on carbon. The agreement between predictions and data is remarkable from quasielastic to deep-inelastic scattering. To test the predicting power of the neural network we consider the numerous kinematical conditions for which experimental cross sections on calcium are available. Furthermore, we show the predictions of the electron-scattering cross sections on oxygen, argon, and titanium: Nuclei of particular interest in the context of present and future accelerator-based neutrino oscillation programs. The agreement between these predictions and the data is comparable to the one of other theoretical models commonly used to calculate electron and neutrino cross sections, such as SuSAv2 and GiBUU. Results obtained with genie, a Monte Carlo event generator, are also discussed for comparison. The good performances obtained with our neural network suggest that neural networks could be exploited for theoretical and experimental investigations of electron- and neutrino-nucleus scattering.