用扩散模型提升星云观测反演，让机器学习更懂物理规律。
Dynamic Diffusion Schrödinger Bridge in Astrophysical Observational Inversions
- 基于配对假设改进扩散模型，适配星云动力学特性。
- 在真实观测数据上预测准确率显著优于传统方法。
- 适合研究天体物理与生成模型交叉的学者参考。
我们研究了扩散薛定谔桥（DSB）模型在动态天体物理系统中的应用，聚焦于巨分子云（GMCs）中恒星形成过程的观测反演任务。提出针对天体物理动力学特性的Astro-DSB模型，采用成对域假设。通过在物理仿真数据和真实观测数据（Taurus B213数据）上的实验，得出两大结论：其一，从天体物理角度看，所提配对DSB方法在可解释性、学习效率和预测性能上均优于传统星统计方法及其他机器学习方法；其二，从生成建模角度看，在包含未见过的初始条件和不同主导物理过程的分布外测试中，概率生成建模表现优于判别式像素到像素建模。本研究拓展了扩散模型在视觉合成之外的应用边界，证明了模型能超越纯数据统计，学习真实物理动态，为未来物理感知生成模型的发展提供支持。
原文摘要 · Abstract (English)
We study Diffusion Schrödinger Bridge (DSB) models in the context of dynamical astrophysical systems, specifically tackling observational inverse prediction tasks within Giant Molecular Clouds (GMCs) for star formation. We introduce the Astro-DSB model, a variant of DSB with the pairwise domain assumption tailored for astrophysical dynamics. By investigating its learning process and prediction performance in both physically simulated data and in real observations (the Taurus B213 data), we present two main takeaways. First, from the astrophysical perspective, our proposed paired DSB method improves interpretability, learning efficiency, and prediction performance over conventional astrostatistical and other machine learning methods. Second, from the generative modeling perspective, probabilistic generative modeling reveals improvements over discriminative pixel-to-pixel modeling in Out-Of-Distribution (OOD) testing cases of physical simulations with unseen initial conditions and different dominant physical processes. Our study expands research into diffusion models beyond the traditional visual synthesis application and provides evidence of the models' learning abilities beyond pure data statistics, paving a path for future physics-aware generative models which can align dynamics between machine learning and real (astro)physical systems.
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