生成扩散先验用于暗宇宙的三维映射
Generative Diffusion Priors for 3D Mapping of the Dark Universe
将扩散模型作为先验，结合可微物理前向模型进行后验采样，解决单视角、噪声观测下的三维重建问题；其plug-and-play后验采样方案可迁移到其他逆问题（如单视图三维重建、计算成像）。
原文依据 we modify a diffusion-based posterior sampling scheme to the 3D weak-lensing setting, combining the learned prior with a differentiable physical forward model.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论从弱引力透镜观测中重建暗物质的三维分布是宇宙学中一个核心但高度不适定的逆问题。与标准的多视角三维重建不同，我们仅从单一视线方向观测宇宙，通过星系形状的噪声畸变进行观测，且星系距离不确定，因此有意义地恢复三维物质场需要强先验假设。现有方法要么使用手工设计的先验产生点估计，要么使用神经网络集成来近似贝叶斯不确定性，难以捕捉宇宙网的非高斯、纤维状结构。随着新的高分辨率宇宙学模拟的出现，我们现在有了另一种先验知识来源，它比解析处方更逼真地捕捉结构形成的非线性统计特性。我们利用这些模拟构建了一个新的数据集\texttt{Conicus3D}，使我们能够学习一个数据驱动的扩散模型先验，捕捉跨越宇宙时间的暗物质结构的完整三维分布。基于最近的即插即用方法，我们将基于扩散的后验采样方案修改为适用于三维弱引力透镜设置，将学习到的先验与可微的物理前向模型相结合。在针对现代弱引力透镜巡天的现实模拟中，我们的方法在二维和三维重建精度上相比基线方法有显著提高。此外，它产生的后验样本的统计特性与底层模拟紧密吻合，同时对宇宙学的中等变化保持鲁棒。
英文原文摘要 1615 字符
Reconstructing the three-dimensional distribution of dark matter from weak-lensing observations is a central but highly ill-posed inverse problem in cosmology. Unlike standard 3D reconstruction with multiple viewpoints, we observe the universe from a single line of sight, through noisy shape distortions of galaxies with uncertain distances, so meaningful recovery of the 3D matter field requires strong prior assumptions. Existing methods either produce point estimates with handcrafted priors or use neural ensembles for approximate Bayesian uncertainty, and struggle to capture the non-Gaussian, filamentary structure of the cosmic web. With the advent of new high-resolution cosmological simulations, we now have an alternative source of prior knowledge that captures the nonlinear statistics of structure formation with far greater fidelity than analytic prescriptions. We leverage these simulations to build a new dataset \texttt Conicus3D , which enables us to learn a data-driven diffusion-model prior capturing the full 3D distribution of dark matter structure across cosmic time. Building on recent plug-and-play approaches, we modify a diffusion-based posterior sampling scheme to the 3D weak-lensing setting, combining the learned prior with a differentiable physical forward model. On realistic simulations targeting a modern weak lensing survey, our approach yields substantially improved 2D and 3D reconstruction accuracy over baseline methods. Moreover, it produces posterior samples whose statistics closely track the underlying simulations, while remaining robust to moderate shifts in cosmology.