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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, Hemant Prasad, and Jan T. Sobczyk

  • *Contact author: beata.kowal@uwr.edu.pl
  • †Contact author: krzysztof.graczyk@uwr.edu.pl
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Phys. Rev. C 112, 055504 – Published 18 November, 2025

DOI: https://doi.org/10.1103/ydvc-2567

Abstract

We present an updated deep neural network model for inclusive electron–carbon scattering. Using the bootstrap model [B. E. Kowal et al., Phys. Rev. C 110, 025501 (2024)], 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.

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Phys. Rev. C 112, 055504– Published 18 November, 2025
  • Received 7 August 2025
  • Accepted 22 October 2025
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