Inclusive electron-nucleus cross section models from domain adaptation

2026-09-08

AI Summary

研究使用迁移学习从碳数据预训练的深度神经网络出发，针对不同核素微调模型以改进电子-核反应截面预测。

Abstract

We apply transfer learning (TL) to construct data-driven models of inclusive electron-nucleus cross sections. Starting from an ensemble of deep neural networks pretrained on \(^{12}\)C data, we fine-tune the models separately for \(^{3}\)He, \(^{6}\)Li, \(^{16}\)O, \(^{27}\)Al, \(^{40}\)Ca, and \(^{56}\)Fe. The resulting models improve for all targets, marginally so for oxygen, where the carbon baseline is already adequate, although their predictive robustness depends on the amount, coverage, and precision of the available target data. We systematically study how model performance depends on the number of fine-tuned layers, on the fraction and selection of the training data, and on the overlap between the source and target kinematic domains. The layer-wise analysis shows that oxygen requires only shallow adaptation, whereas helium, calcium, and iron require substantially deeper fine-tuning. Lithium represents the least robust case because of its limited dataset, while aluminum demonstrates a strong sensitivity to a small subset of highly constraining measurements. For selected kinematic configurations outside the coverage of the carbon training data, the adapted models remain consistent with the measurements within their estimated uncertainties. Finally, we compare the resulting predictions with those of the phenomenological F1F2 model.

Problem

• transfer learning
• inclusive electron-nucleus cross sections
• data-driven models
• fine-tuning

Innovation

• transfer learning
• data-driven models
• inclusive electron-nucleus cross sections
• fine-tuning
• kinematic domains