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

arXiv: https://arxiv.org/abs/2508.00996 · HTML: https://mlsanity.com/paper/2508.00996/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Builds on (most cited first, 6 of 6)

- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [A high-bias, low-variance introduction to Machine Learning for physicists](https://mlsanity.com/paper/1803.08823/) (2018)
- [Predicting Porosity, Permeability, and Tortuosity of Porous Media from Images by Deep Learning](https://mlsanity.com/paper/2007.02820/) (2020)
- [Electron-nucleus cross sections from transfer learning](https://mlsanity.com/paper/2408.09936/) (2024)
- [Deep learning for diffusion in porous media](https://mlsanity.com/paper/2304.02104/) (2023)
- [Machine Learning Neutrino-Nucleus Cross Sections](https://mlsanity.com/paper/2412.16303/) (2024)

## Abstract (arXiv)

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.