用扩散模型实现天体图像去卷积，量化重建结果的不确定性。
Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions
- 基于贝叶斯框架的扩散模型，结合高分辨率宇宙模拟训练。
- 在HSC数据上实现接近哈勃望远镜的分辨率，还原细节清晰。
- 提出新指标识别先验驱动特征，适合严谨天文研究使用。
天体图像去卷积是恢复地面观测中天体真实属性的关键。本文探索利用扩散模型（DMs）与扩散后验采样（DPS）算法解决该逆问题。通过在高分辨率宇宙模拟数据上训练基于得分的扩散模型，并在贝叶斯框架下计算给定观测的后验分布，将红移和像素尺度作为逆问题参数，使方法可适配任意数据集。我们在超广角巡天相机（HSC）数据上测试模型，结果显示重建分辨率接近哈勃空间望远镜（HST）水平。更重要的是，我们量化了重建结果的不确定性，并提出一种新度量来识别重建图像中由先验驱动的特征，这对科学应用至关重要。
原文摘要 · Abstract (English)
Deconvolution of astronomical images is a key aspect of recovering the intrinsic properties of celestial objects, especially when considering ground-based observations. This paper explores the use of diffusion models (DMs) and the Diffusion Posterior Sampling (DPS) algorithm to solve this inverse problem task. We apply score-based DMs trained on high-resolution cosmological simulations, through a Bayesian setting to compute a posterior distribution given the observations available. By considering the redshift and the pixel scale as parameters of our inverse problem, the tool can be easily adapted to any dataset. We test our model on Hyper Supreme Camera (HSC) data and show that we reach resolutions comparable to those obtained by Hubble Space Telescope (HST) images. Most importantly, we quantify the uncertainty of reconstructions and propose a metric to identify prior-driven features in the reconstructed images, which is key in view of applying these methods for scientific purposes.
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