用生成对抗网络迁移学习，让中微子散射模拟更高效准确。
Transfer Learning for Neutrino Scattering: Domain Adaptation with GANs
- 用生成对抗网络迁移学习，复用碳靶数据建模氩靶和反中微子散射。
- 在1万到10万事件的小样本下仍保持高精度，优于从零训练模型。
- 适合实验数据少的中微子物理研究，提升下一代事件生成器性能。
迁移学习（TL）被用于将基于合成中微子-碳核散射数据训练的生成对抗网络（GAN）所编码的物理信息，外推至中微子-氩和反中微子-碳相互作用等关联过程。我们探究了不同靶核与过程中轻子-核动力学的共享程度，并评估了当训练数据来自不同中微子-核相互作用模型时迁移学习的有效性。结果表明，迁移学习不仅能重现轻子动量分布中的关键特征，如准弹性峰和Δ共振峰，且显著优于从零开始训练的生成模型。使用10,000和100,000事件的数据集，迁移学习在统计量有限的情况下仍保持高精度。研究证明，迁移学习为建模（反）中微子-核相互作用提供了一个合理且高效的框架，尤其在实验数据稀疏时具有重要价值，适用于下一代中微子散射事件生成器的构建。
原文摘要 · Abstract (English)
Transfer learning (TL) is used to extrapolate the physics information encoded in a Generative Adversarial Network (GAN) trained on synthetic neutrino-carbon inclusive scattering data to related processes such as neutrino-argon and antineutrino-carbon interactions. We investigate how much of the underlying lepton-nucleus dynamics is shared across different targets and processes. We also assess the effectiveness of TL when training data is obtained from a different neutrino-nucleus interaction model. Our results show that TL not only reproduces key features of lepton kinematics, including the quasielastic and $Δ$-resonance peaks, but also significantly outperforms generative models trained from scratch. Using data sets of 10,000 and 100,000 events, we find that TL maintains high accuracy even with limited statistics. Our findings demonstrate that TL provides a well-motivated and efficient framework for modeling (anti)neutrino-nucleus interactions and for constructing next-generation neutrino-scattering event generators, particularly valuable when experimental data are sparse.
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