Re-optimization of a deep neural network model for electron-carbon scattering using new experimental data
High Energy Physics - Phenomenology
2025-11-21 v2 Machine Learning
Nuclear Experiment
Nuclear Theory
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.
Keywords
Cite
@article{arxiv.2508.00996,
title = {Re-optimization of a deep neural network model for electron-carbon scattering using new experimental data},
author = {Beata E. Kowal and Krzysztof M. Graczyk and Artur M. Ankowski and Rwik Dharmapal Banerjee and Jose L. Bonilla and Hemant Prasad and Jan T. Sobczyk},
journal= {arXiv preprint arXiv:2508.00996},
year = {2025}
}
Comments
15 pages, 12 figures, some additional comments added