Re-optimization of a deep neural network model for electron-carbon scattering using new experimental data
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915627052040192 |
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| author | Kowal, Beata E. Graczyk, Krzysztof M. Ankowski, Artur M. Banerjee, Rwik Dharmapal Bonilla, Jose L. Prasad, Hemant Sobczyk, Jan T. |
| author_facet | Kowal, Beata E. Graczyk, Krzysztof M. Ankowski, Artur M. Banerjee, Rwik Dharmapal Bonilla, Jose L. Prasad, Hemant Sobczyk, Jan T. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_00996 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Re-optimization of a deep neural network model for electron-carbon scattering using new experimental data Kowal, Beata E. Graczyk, Krzysztof M. Ankowski, Artur M. Banerjee, Rwik Dharmapal Bonilla, Jose L. Prasad, Hemant Sobczyk, Jan T. High Energy Physics - Phenomenology Machine Learning Nuclear Experiment Nuclear Theory 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. |
| title | Re-optimization of a deep neural network model for electron-carbon scattering using new experimental data |
| topic | High Energy Physics - Phenomenology Machine Learning Nuclear Experiment Nuclear Theory |
| url | https://arxiv.org/abs/2508.00996 |