Re-optimization of a deep neural network model for electron-carbon scattering using new experimental data
This work addresses the need for more accurate cross-section models in particle physics experiments, but it is incremental as it builds on an existing prior model.
The authors tackled the problem of improving predictions for electron-carbon scattering cross-sections by re-optimizing a deep neural network model using new and older experimental data, resulting in updated predictions with uncertainties evaluated for experiments like Hyper-Kamiokande and DUNE.
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.