Neural Posterior Estimation for Tomographic Weak Lensing Mass Mapping
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 ...
| Authors | Tim White, Shreyas Chandrashekaran, Camille Avestruz et al. |
|---|---|
| Primary category | astro-ph.IM |
| Published | 7 September 2026 |
| Last updated at source | 7 September 2026 |
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