Neural Posterior Estimation for Inferring Weak Lensing Shear
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 de...
| Authors | Tim White, Dingrui Tao, Camille Avestruz et al. |
|---|---|
| Primary category | astro-ph.IM |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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