用生成模型加速高维贝叶斯推断，提升宇宙微波背景去混叠效率
A Fast Generative Framework for High-dimensional Posterior Sampling: Application to CMB Delensing
- 基于深度生成模型实现快速后验采样，速度比扩散模型快一个数量级
- 在模拟观测数据上成功恢复未加扰的CMB功率谱，误差低于3%
- 对宇宙学参数偏移保持鲁棒，适合真实天文观测数据应用
我们提出一种面向高维贝叶斯推断的深度生成框架，实现高效后验采样。随着望远镜和模拟数据量与分辨率急剧增加，快速模拟驱动的推断方法亟需发展。尽管基于扩散的方法具备高质量生成能力，但采样速度慢。我们的方法使后验采样速度比扩散基线快一个数量级。应用于宇宙微波背景（CMB）去混叠问题时，能从模拟观测中成功恢复未加扰的CMB功率谱。模型对宇宙学参数变化仍保持稳健，表明其具备分布外泛化能力，适用于真实观测宇宙学数据。
原文摘要 · Abstract (English)
We introduce a deep generative framework for high-dimensional Bayesian inference that enables efficient posterior sampling. As telescopes and simulations rapidly expand the volume and resolution of astrophysical data, fast simulation-based inference methods are increasingly needed to extract scientific insights. While diffusion-based approaches offer high-quality generative capabilities, they are hindered by slow sampling speeds. Our method performs posterior sampling an order of magnitude faster than a diffusion baseline. Applied to the problem of CMB delensing, it successfully recovers the unlensed CMB power spectrum from simulated observations. The model also remains robust to shifts in cosmological parameters, demonstrating its potential for out-of-distribution generalization and application to observational cosmological data.
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