用无监督域适应提升神经后验估计，让引力透镜分析更准更快。
Domain-Adaptive Neural Posterior Estimation for Strong Gravitational Lens Analysis
- 结合无监督域适应与神经后验估计，解决仿真数据到真实观测的迁移问题。
- 在含噪声的真实数据上，推断精度提升1-2个数量级，后验覆盖率显著改善。
- 适合从事引力透镜、宇宙学数据分析的研究者，尤其关注高效推断方法者。
强引力透镜建模对现代及下一代宇宙巡天数据而言计算成本极高。神经后验估计（NPE）作为基于模拟的推断（SBI）方法，被视为高效分析强透镜数据的潜在路径。然而，此前尚未证明NPE在域外目标数据（如训练于仿真数据后应用于真实观测数据）上表现良好。本文首次研究了将无监督域适应（UDA）与NPE结合的可行性。源域为无噪声仿真数据，目标域加入模拟现代宇宙学巡天噪声的数据。结果表明，结合UDA与NPE可使推断精度提高1-2个数量级，并显著改善后验覆盖性能，优于未使用UDA的NPE模型。我们预计该方法组合将推动未来NPE模型在真实观测数据中的应用。
原文摘要 · Abstract (English)
Modeling strong gravitational lenses is prohibitively expensive for modern and next-generation cosmic survey data. Neural posterior estimation (NPE), a simulation-based inference (SBI) approach, has been studied as an avenue for efficient analysis of strong lensing data. However, NPE has not been demonstrated to perform well on out-of-domain target data -- e.g., when trained on simulated data and then applied to real, observational data. In this work, we perform the first study of the efficacy of NPE in combination with unsupervised domain adaptation (UDA). The source domain is noiseless, and the target domain has noise mimicking modern cosmology surveys. We find that combining UDA and NPE improves the accuracy of the inference by 1-2 orders of magnitude and significantly improves the posterior coverage over an NPE model without UDA. We anticipate that this combination of approaches will help enable future applications of NPE models to real observational data.
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