一种快速生成框架用于高维后验采样：应用于宇宙微波背景去棱镜化
A Fast Generative Framework for High-dimensional Posterior Sampling: Application to CMB Delensing
AI总结：
本文提出了一种快速生成框架，用于高维后验采样，应用于宇宙微波背景去棱镜化问题，实现了比扩散方法快一个数量级的采样速度，并成功恢复了未棱镜化的宇宙微波背景功率谱。
摘要：
我们介绍了一种深度生成框架，用于高维贝叶斯推断，能够实现高效的后验采样。随着望远镜和模拟快速扩大天体物理数据的体积和分辨率，快速基于模拟的推断方法越来越需要用来提取科学见解。虽然基于扩散的方法提供了高质量的生成能力，但它们受到采样速度慢的限制。我们的方法比基于扩散的基线快一个数量级。应用于宇宙微波背景去棱镜化问题时，它成功地从模拟观测中恢复了未棱镜化的宇宙微波背景功率谱。该模型也对宇宙学参数的变化保持稳健，展示了其在超出分布泛化和应用于观测宇宙学数据方面的潜力。
英文摘要：
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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