用标准模型预训练，减少新物理模拟需求，但需防负迁移。
Transfer Learning Beyond the Standard Model
- 在ΛCDM上预训练，再微调到新物理场景，降低仿真成本。
- 仅需少量新物理仿真即可实现准确推断，提升效率。
- 瓶颈结构效果最佳，但强参数混淆会导致学习失败。
机器学习可实现强大的宇宙学推断，但通常需要大量覆盖多种宇宙学模型的高保真模拟。迁移学习通过跨模型复用知识，有望降低模拟成本。我们发现，在标准宇宙学模型ΛCDM上进行预训练，并在各种超越ΛCDM的场景（包括大质量中微子、修改引力、原初非高斯性）上微调，可显著减少对超越ΛCDM模拟的需求。然而，当ΛCDM与超越ΛCDM参数之间存在强烈物理退化时，也可能出现负迁移。我们测试了多种迁移架构，发现引入瓶颈结构表现最优。研究揭示了物理领域基础模型方法的机遇与风险：预训练可加速推断，但也可能阻碍新物理的学习。
原文摘要 · Abstract (English)
Machine learning enables powerful cosmological inference but typically requires many high-fidelity simulations covering many cosmological models. Transfer learning offers a way to reduce the simulation cost by reusing knowledge across models. We show that pre-training on the standard model of cosmology, $Λ$CDM, and fine-tuning on various beyond-$Λ$CDM scenarios -- including massive neutrinos, modified gravity, and primordial non-Gaussianities -- can enable inference with significantly fewer beyond-$Λ$CDM simulations. However, we also show that negative transfer can occur when strong physical degeneracies exist between $Λ$CDM and beyond-$Λ$CDM parameters. We consider various transfer architectures, finding that including bottleneck structures provides the best performance. Our findings illustrate the opportunities and pitfalls of foundation-model approaches in physics: pre-training can accelerate inference, but may also hinder learning new physics.
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