Towards solving model bias in cosmic shear forward modeling

TL;DR

This paper introduces a hybrid physical and deep learning Hierarchical Bayesian Model that effectively eliminates model bias in cosmic shear measurements, improving the accuracy of weak gravitational lensing analysis.

Contribution

It presents a novel hybrid model combining physical galaxy morphology with deep learning to achieve unbiased shear estimation in cosmic shear studies.

Findings

Unbiased shear estimates on realistic galaxy models

Effective mitigation of model bias in shear measurement

Enhanced accuracy in weak lensing cosmological probes

Abstract

As the volume and quality of modern galaxy surveys increase, so does the difficulty of measuring the cosmological signal imprinted in galaxy shapes. Weak gravitational lensing sourced by the most massive structures in the Universe generates a slight shearing of galaxy morphologies called cosmic shear, key probe for cosmological models. Modern techniques of shear estimation based on statistics of ellipticity measurements suffer from the fact that the ellipticity is not a well-defined quantity for arbitrary galaxy light profiles, biasing the shear estimation. We show that a hybrid physical and deep learning Hierarchical Bayesian Model, where a generative model captures the galaxy morphology, enables us to recover an unbiased estimate of the shear on realistic galaxies, thus solving the model bias.