层析弱引力透镜质量制图的神经后验估计
Neural Posterior Estimation for Tomographic Weak Lensing Mass Mapping
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中文总结 AI 辅助
本文提出神经后验估计（NPE）方法，训练深度神经网络将多波段图像直接映射为层析剪切和会聚场的变分分布，实现快速、摊销的场级弱透镜推断，并在DC2模拟数据上验证了其校准性与准确性。
中文摘要 AI 辅助
弱引力透镜的剪切和会聚场追踪了重子物质和暗物质在空间中的分布，使其成为宇宙结构的强大探针。从图像中推断剪切和会聚是一个具有挑战性的逆问题。目前的主流方法是从星系椭率的加权平均值估计剪切，校准这些估计以考虑系统偏差，并将其变换以重建会聚场，这是一个多阶段过程，需要大量的计算资源和对统计不确定性的细致处理。作为替代方案，我们提出了一种场级弱透镜推断的概率方法，其中我们训练一个深度神经网络，直接将多波段图像映射到潜在层析剪切和会聚场上的变分分布。这种神经后验估计（NPE）过程隐式地边缘化了宇宙学前向模型中的干扰变量，并且不需要评估似然函数。它也是摊销的，因此一旦神经网络训练完成，就能为天文巡天实现快速的后验推断。当在LSST-DESC DC2模拟天空巡天的合成图像上进行评估时，NPE为剪切和会聚产生了与地面真值一致的校准良好的变分分布。我们描述了如何将这些变分分布中采样的图用于后续的基于模拟的推断过程，以近似宇宙学参数的后验分布。
英文摘要
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.
发表机构
- University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。
