Re-optimization of a deep neural network model for electron-carbon scattering using new experimental data

TL;DR

This paper updates a deep neural network model for electron-carbon scattering by incorporating new experimental data, improving predictions and uncertainties relevant for neutrino experiments like Hyper-Kamiokande and DUNE.

Contribution

It introduces a re-optimized neural network model that integrates recent experimental data to enhance scattering predictions and uncertainty quantification.

Findings

Improved cross-section predictions for electron-carbon scattering.

Enhanced uncertainty estimates in the model.

Validated predictions within the kinematic range of major neutrino experiments.

Abstract

We present an updated deep neural network model for inclusive electron-carbon scattering. Using the bootstrap model [Phys.Rev.C 110 (2024) 2, 025501] as a prior, we incorporate recent experimental data, as well as older measurements in the deep inelastic scattering region, to derive a re-optimized posterior model. We examine the impact of these new inputs on model predictions and associated uncertainties. Finally, we evaluate the resulting cross-section predictions in the kinematic range relevant to the Hyper-Kamiokande and DUNE experiments.