Inclusive electron-nucleus cross section models from domain adaptation
This paper applies transfer learning to fine-tune deep neural networks pretrained on carbon-12 data, successfully constructing robust, data-driven models for inclusive electron-nucleus cross sections across various nuclei that outperform existing phenomenological models while systematically analyzing the impact of fine-tuning depth, data availability, and kinematic coverage.