{"ID":2888089,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.00996","arxiv_id":"2508.00996","title":"Re-optimization of a deep neural network model for electron-carbon scattering using new experimental data","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.","short_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 e...","url_abs":"https://arxiv.org/abs/2508.00996","url_pdf":"https://arxiv.org/pdf/2508.00996v2","authors":"[\"Beata E. Kowal\",\"Krzysztof M. Graczyk\",\"Artur M. Ankowski\",\"Rwik Dharmapal Banerjee\",\"Jose L. Bonilla\",\"Hemant Prasad\",\"Jan T. Sobczyk\"]","published":"2025-08-01T18:05:38Z","proceeding":"hep-ph","tasks":"[\"hep-ph\",\"cs.LG\",\"nucl-ex\",\"nucl-th\"]","methods":"[]","has_code":false}