提出新方法解决物理仿真与实验数据间的本质差异问题
Safe Domain Adaptation for Physics: Overcoming Nuisances, Label Shifts, and Simulation Priors
- 通过重加权仿真事件，聚焦真实物理差异而非模拟偏差
- 在能量谱不一致时避免引入模拟先验导致的错误对齐
- 无需标签即可选择最优模型配置，适合物理领域迁移学习
领域自适应广泛用于将训练于仿真的神经网络应用于实验数据。其前提是两个领域仅存在无关扰动，且目标量分布相同。但在物理学中，这两点均不成立：仿真可能错误建模物理规律，而目标量（如能谱、红移分布）本身即是观测结果。本文在简化空气簇射基准任务中研究了探测器响应噪声、物理仿真偏移和能谱变化的单独或联合影响。标准对抗性自适应可处理条件偏移，但一旦能谱不同，便会强制对齐，以模拟先验为锚点引入可控偏差。本文提出自适应领域自适应方法，通过重加权仿真事件，使自适应过程仅聚焦于真实的物理差异。由于预测谱依赖于训练配置，我们提出了无标签的模型选择准则，用于选取接近最优的运行点。
原文摘要 · Abstract (English)
Domain adaptation is widely used to make neural networks trained on simulations applicable to experimental data. Its premise is that the two domains differ only in nuisances, and that the quantity of interest is distributed identically in both. In physics neither assumption holds: simulations can be wrong about the physics, and the distribution of the target quantity - an energy spectrum, a redshift distribution - is often the measurement itself. We study the consequences of such mismatches on a toy air-shower benchmark in which a detector-response nuisance, a physical simulation shift, and an energy-spectrum shift can be switched on separately or together. Standard adversarial adaptation handles the conditional shifts, but once the two spectra differ it aligns them, replacing an uncontrolled bias by one anchored on the simulation prior. We present adaptive domain adaptation, which reweights the simulated events so as to focus domain adaptation on the genuine physical mismatch alone. Since the predicted spectrum depends on model training configuration, we provide a label-free model selection rule for selecting the near-the-best operation point.
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