用得分模型同时重建引力透镜源和质量分布，首次实现无偏联合反演。
Blind Strong Gravitational Lensing Inversion: Joint Inference of Source and Lens Mass with Score-Based Models
- 基于连续时间吉布斯采样，联合推断源图像与参数化透镜质量分布。
- 重建残差符合观测噪声水平，透镜参数后验均值无系统偏差。
- 适合从事天体物理反演、贝叶斯推理的科研人员参考。
得分模型可作为科学逆问题中表达性强的数据驱动先验。在强引力透镜中，它们能从多重成像观测中推断背景星系的后验分布。以往工作假设透镜质量分布（即前向算子）已知，本文通过基于GibbsDDRM的采样器，在连续时间下联合推断源图像与参数化透镜质量分布，实现了对源与透镜的联合反演。重建结果残差与观测噪声一致，透镜参数的边际后验分布能准确恢复真实值，无系统偏差。据我们所知，这是首个成功使用得分模型先验实现源与透镜联合推断的研究。
原文摘要 · Abstract (English)
Score-based models can serve as expressive, data-driven priors for scientific inverse problems. In strong gravitational lensing, they enable posterior inference of a background galaxy from its distorted, multiply-imaged observation. Previous work, however, assumes that the lens mass distribution (and thus the forward operator) is known. We relax this assumption by jointly inferring the source and a parametric lens-mass profile, using a sampler based on GibbsDDRM but operating in continuous time. The resulting reconstructions yield residuals consistent with the observational noise, and the marginal posteriors of the lens parameters recover true values without systematic bias. To our knowledge, this is the first successful demonstration of joint source-and-lens inference with a score-based prior.
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