Neural Posterior Estimation for Inferring Weak Lensing Shear
2026-07-10 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors suggest a new way to measure weak gravitational lensing, which usually involves complicated steps like finding galaxies and correcting errors. Instead, they use a type of artificial intelligence called neural posterior estimation, training a network to directly estimate the lensing effect from simulated images. Their method combines multiple tasks into one and produces accurate results even when images have stars, overlapping galaxies, and other complications. This approach works well as long as the simulations match real observations closely enough.
weak gravitational lensingshear estimationneural posterior estimationsimulation-based inferencegalaxy detectiondeblendingpoint spread functionimage calibrationposterior approximationdetector artifacts
Authors
Tim White, Dingrui Tao, Camille Avestruz, Jeffrey Regier, the LSST Dark Energy Science Collaboration
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.