发表机构
Department of Statistics, University of Michigan; Department of Physics, University of Michigan（密歇根大学统计学系; 密歇根大学物理学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究提出用神经后验估计（NPE）推断弱引力透镜切变，训练深度神经网络将模拟多波段图像映射到切变场变分分布，整合多步骤为隐式推理，实验表明在复杂观测效应下NPE能产生准确校准的后验近似，是可行的切变估计方法。
AI 中文摘要
从图像推断弱引力透镜切变的主流方法包括检测星系、估计其椭圆率并校准这些估计以校正图像噪声、选择偏差和模型错误设定。表征此流程背后的统计模型和假设具有挑战性，难以在各个阶段传播不确定性。作为替代方案，我们提议使用神经后验估计（NPE）来推断切变，这是一种基于模拟的推理。我们训练一个深度神经网络，将模拟的多波段图像映射到潜在切变场的变分分布，从而将星系检测、去混合、测量和校准整合到一个单一的隐式推理步骤中。一旦训练完成，网络会考虑模拟图像中存在的所有特征，包括潜在的偏差源。在具有日益复杂观测效应的模拟恒定切变图像实验中，NPE在存在混合星系、空间变化的点扩散函数、恒星和探测器伪像的情况下，为两个切变分量产生了准确且校准良好的后验近似。这些结果表明，在所有预期特征和伪像都可以模拟的情况下，NPE可以是一种可行的切变估计方法，随着未来几十年模拟保真度的提高，这一要求将变得越来越可行。
英文摘要
The prevailing approach to inferring weak gravitational lensing shear from images involves detecting galaxies, estimating their ellipticities, and calibrating these estimates to correct for image noise, selection bias, and model misspecification. Characterizing the statistical model and assumptions underlying this pipeline is challenging, which makes it difficult to propagate uncertainty through its various stages. As an alternative, we propose to infer shear using neural posterior estimation (NPE), a type of simulation-based inference. We train a deep neural network to map a simulated multiband image to a variational distribution over the underlying shear field, thereby folding galaxy detection, deblending, measurement, and calibration into a single implicit inference step. Once trained, the network accounts for all features present in the simulated images, including potential sources of bias. In experiments on simulated constant-shear images with increasingly complex observational effects, NPE produces accurate and well-calibrated posterior approximations for both shear components in the presence of blended galaxies, spatially varying point spread functions, stars, and detector artifacts. These results demonstrate that NPE can be a viable shear estimation method in settings where all anticipated features and artifacts can be simulated, a requirement that will become increasingly feasible as simulation fidelity improves in the coming decades.
Comments17 pages, 10 figures, 3 tables
