论文标题
可伸缩的贝叶斯推断，以找到强力镜头
Scalable Bayesian Inference for Finding Strong Gravitational Lenses
论文作者
论文摘要
在天文图像中找到强烈的引力镜头使我们能够评估宇宙学理论并了解宇宙的大规模结构。先前关于晶状体检测的工作不会在现代调查中量化镜头参数估计或尺度的不确定性。我们提出了一种完全摊销的贝叶斯程序，以克服这些局限性。与传统的变分推断不同，训练可以最大程度地减少反向kullback-leibler（KL）差异，我们的方法接受了预期的前锋KL差异的训练。使用合成的Galsim图像和真实的Sloan数字天空调查（SDSS）图像，我们证明了经过正向KL训练的推理在镜头检测和参数估计中都会产生良好的不确定性。
Finding strong gravitational lenses in astronomical images allows us to assess cosmological theories and understand the large-scale structure of the universe. Previous works on lens detection do not quantify uncertainties in lens parameter estimates or scale to modern surveys. We present a fully amortized Bayesian procedure for lens detection that overcomes these limitations. Unlike traditional variational inference, in which training minimizes the reverse Kullback-Leibler (KL) divergence, our method is trained with an expected forward KL divergence. Using synthetic GalSim images and real Sloan Digital Sky Survey (SDSS) images, we demonstrate that amortized inference trained with the forward KL produces well-calibrated uncertainties in both lens detection and parameter estimation.
