Scalable Bayesian Inference for Finding Strong Gravitational Lenses
Scalable Bayesian Inference for Finding Strong Gravitational Lenses
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发表时间：
2022-11
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通讯作者：
Yash J. Patel;J. Regier
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作者：
Yash J. Patel;J. Regier
在天文图像中发现强引力透镜使我们能够评估宇宙学理论并理解宇宙的大尺度结构。以前的工作透镜检测不量化的不确定性透镜参数估计或规模现代调查。我们提出了一个完全摊销的贝叶斯过程透镜检测，克服了这些限制。与传统的变分推理不同，在传统的变分推理中，训练最小化反向Kullback-Leibler（KL）发散，我们的方法是用预期的正向KL发散训练的。使用合成GalSim图像和真实的斯隆数字巡天（SDSS）图像，我们证明，使用前向KL训练的摊销推理在透镜检测和参数估计中产生了良好校准的不确定性。
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.