Neural Posterior Estimation for Tomographic Weak Lensing Mass Mapping

2026-09-07

AI Summary

本文提出了一种基于深度神经网络的概率方法，直接从多波段图像映射到弱引力透镜的剪切和收敛场的变分分布，以解决传统方法计算资源消耗大、处理复杂的问题。

Abstract

Weak gravitational lensing shear and convergence trace the distribution of baryonic and dark matter across space, making them a powerful probe of cosmic structure. Inferring shear and convergence from images is a challenging inverse problem. The prevailing approach to this task estimates shear from weighted averages of galaxy ellipticities, calibrates these estimates to account for systematic biases, and transforms them to reconstruct convergence, a multistage procedure that requires substantial computational resources and meticulous handling of statistical uncertainties. As an alternative, we propose a probabilistic approach to field-level weak lensing inference in which we train a deep neural network to directly map a multiband image to a variational distribution over the underlying tomographic shear and convergence fields. This neural posterior estimation (NPE) procedure implicitly marginalizes over nuisance variables in the cosmological forward model and does not require evaluating the likelihood function. It is also amortized, so it enables rapid posterior inference for astronomical surveys once the neural network is trained. When evaluated on synthetic images from the LSST-DESC DC2 Simulated Sky Survey, NPE produces well-calibrated variational distributions for shear and convergence that are consistent with the ground truth. We describe how maps sampled from these variational distributions could be used in a subsequent simulation-based inference procedure to approximate the posterior distribution over cosmological parameters.

Problem

• weak lensing
• shear and convergence
• inverse problem
• cosmological parameters
• neural posterior estimation

Innovation

• Neural Posterior Estimation
• variational distribution
• amortized inference
• weak lensing
• cosmological parameters