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.
翻译:为高维显示引力分散源的亮度而精确推断出精确的后方体是一个重大挑战,部分原因是难以准确量化前方数据。在这里,我们报告使用一个基于分数的模型来编码背景星系未经扭曲的图像的推论前方数据。该模型在一组非扭曲星系高分辨率图像方面受过培训。通过将概率分数加到前一个分数,并使用逆时蒸汽差方程式求解器,我们从后方获取样本。我们的方法产生独立的远方样本,并模拟几乎接近噪音水平的数据。我们展示了可能性和先前的平衡如何在对分布数据进行实验时达到我们的期望。