用生成模型先验提升射电干涉图像重建质量
IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors
- 基于分数生成模型构建图像先验，结合可见性数据建模
- 在DSHARP数据上生成合理后验样本，即使先验不匹配也有效
- 适合射电天文图像重建，尤其对低信噪比数据有优势
在射电天文学中，从噪声干扰的干涉数据中推断天空表面亮度分布一直是一个关键挑战。本文提出一种名为IRIS的方法，利用在星系光学图像上训练的分数生成模型作为表达性强的先验，结合uv空间中的高斯似然，从阿尔玛望远镜（ALMA）开展的DSHARP调查的可见性数据中重建原行星盘图像。与传统成像算法相比，该框架即使使用不准确的星系先验，仍能生成合理的后验样本。通过模拟数据的覆盖测试，我们实证评估了该方法生成校准后验样本的准确性。
原文摘要 · Abstract (English)
Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challenge in radio astronomy. In this work, we introduce Imaging for Radio Interferometry with Score-based models (IRIS). We use score-based models trained on optical images of galaxies as an expressive prior in combination with a Gaussian likelihood in the uv-space to infer images of protoplanetary disks from visibility data of the DSHARP survey conducted by ALMA. We demonstrate the advantages of this framework compared with traditional radio interferometry imaging algorithms, showing that it produces plausible posterior samples despite the use of a misspecified galaxy prior. Through coverage testing on simulations, we empirically evaluate the accuracy of this approach to generate calibrated posterior samples.
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