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

Beata E. Kowal, Krzysztof M. Graczyk, Artur M. Ankowski, Rwik Dharmapal Banerjee, Jose L. Bonilla +2 more
8/1/2025
hep-phcs.LGnucl-ex

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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.

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Cite this paper

@article{kowal2025reoptimization,
  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},
  year   = {2025},
  eprint = {2508.00996},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2508.00996v2}
}

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