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

We present an updated deep neural network model for inclusive electron–carbon scattering. Using the bootstrap model [B. E. Kowal ., ], we incorporate recent experimental data, as well as older measurements in the deep inelastic scattering region, to derive a reoptimized 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.