论文标题
通过降解扩散恢复模型的强透明源重建
Strong-Lensing Source Reconstruction with Denoising Diffusion Restoration Models
论文作者
论文摘要
银河系 - galaxy强镜头系统的分析在很大程度上取决于对源外观的任何先前假设。在这里，我们提出了一种基于denoising扩散概率模型（DDPM）的源星系的数据驱动的先验 /正则化的方法。我们使用预先训练的模型来用于星系图像，Astroddpm和一系列称为DeNoising扩散重建模型（DDRM）的有条件重建步骤，以获得与噪音观察和训练数据的分布相一致的样品。我们表明，这些样品具有与源模型后验相关的定性特性：在低到中噪声的情况下，它们非常类似于观察结果，而来自不确定数据的重建则显示出更大的可变性，这与使用为先前的生成模型中编码的分布一致。
Analysis of galaxy--galaxy strong lensing systems is strongly dependent on any prior assumptions made about the appearance of the source. Here we present a method of imposing a data-driven prior / regularisation for source galaxies based on denoising diffusion probabilistic models (DDPMs). We use a pre-trained model for galaxy images, AstroDDPM, and a chain of conditional reconstruction steps called denoising diffusion reconstruction model (DDRM) to obtain samples consistent both with the noisy observation and with the distribution of training data for AstroDDPM. We show that these samples have the qualitative properties associated with the posterior for the source model: in a low-to-medium noise scenario they closely resemble the observation, while reconstructions from uncertain data show greater variability, consistent with the distribution encoded in the generative model used as prior.
