论文标题
具有基于得分的先验的强力镜头中源星系的后源样品
Posterior samples of source galaxies in strong gravitational lenses with score-based priors
论文作者
论文摘要
推断出重力镜头亮度的高维度表示的准确后代是一个主要挑战，部分原因是难以准确量化先验。在这里，我们报告了基于得分的模型的使用来编码背景星系未变形图像的推断。该模型经过一组未发生星系的高分辨率图像的训练。通过将似然得分添加到先前的分数并使用反向随机微分方程求解器，我们从后部获得样品。我们的方法产生独立的后验样品，并将数据几乎降低到噪声水平。我们展示了可能性与先前的期望之间的平衡，并在分布数据外的实验中达到了我们的期望。
Inferring accurate posteriors for high-dimensional representations of the brightness of gravitationally-lensed sources is a major challenge, in part due to the difficulties of accurately quantifying the priors. Here, we report the use of a score-based model to encode the prior for the inference of undistorted images of background galaxies. This model is trained on a set of high-resolution images of undistorted galaxies. By adding the likelihood score to the prior score and using a reverse-time stochastic differential equation solver, we obtain samples from the posterior. Our method produces independent posterior samples and models the data almost down to the noise level. We show how the balance between the likelihood and the prior meet our expectations in an experiment with out-of-distribution data.
