Neural Posterior Estimation for Inferring Weak Lensing Shear

2026-07-10

AI Summary

This work addresses the fragmented nature of traditional weak gravitational lensing shear inference pipelines, which struggle to jointly model and propagate uncertainties across distinct processing stages. The study introduces, for the first time, simulation-based neural posterior estimation to this task, proposing an end-to-end deep learning framework that directly regresses the variational posterior distribution of the shear field from multi-band simulated images. This approach implicitly integrates galaxy detection, deblending, shape measurement, and calibration into a unified model. Evaluated on complex simulations incorporating galaxy blending, spatially varying point spread functions, and detector artifacts, the method accurately and well-calibratedly recovers both shear components, enabling joint modeling and quantification of bias and uncertainty.

Abstract

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.

Problem

• weak lensing shear
• uncertainty propagation
• statistical modeling
• simulation-based inference
• posterior estimation

Innovation

• Neural Posterior Estimation
• simulation-based inference
• weak lensing shear
• deep learning
• uncertainty quantification