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.

翻译：在天文图像中找到强大的引力透镜使我们能够评估宇宙理论并了解宇宙的大规模结构。 先前的透镜探测工作没有将镜头参数估计或规模的不确定性量化到现代测量中。 我们展示了完全摊销的贝ysian透镜检测程序,克服了这些限制。 与传统的变异推断方法不同的是,培训最大限度地缩小了Kullback-Leiber(KL)的反向差异,我们的方法经过了预期前方KL差异的培训。 利用合成的GalSim图像和真正的Sloan数字天空测量(SDSS)图像,我们展示了与前方KL培训的摊销推推法在透镜检测和参数估计中都产生了精确的不确定性。