Galaxy shear estimation from stacked images

TL;DR

This paper explores a robust galaxy shear estimation method using image stacking to produce Gaussian-like images, simplifying shear measurement by focusing on PSF modeling, and evaluates its performance on toy simulations.

Contribution

It introduces a stacking-based shear estimation approach that is potentially more robust and less model-dependent than traditional methods, especially under high galaxy density conditions.

Findings

Stacking produces nearly Gaussian images, simplifying shear estimation.

The method performs well on toy simulations with constant PSF and shear.

It provides a baseline for bias estimation and robustness testing.

Abstract

Statistics of the weak lensing of galaxies can be used to constrain cosmology if the galaxy shear can be estimated accurately. In general this requires accurate modelling of unlensed galaxy shapes and the point spread function (PSF). I discuss suboptimal but potentially robust methods for estimating galaxy shear by stacking images such that the stacked image distribution is closely Gaussian by the central limit theorem. The shear can then be determined by radial fitting, requiring only an accurate model of the PSF rather than also needing to model each galaxy accurately. When noise is significant asymmetric errors in the centroid must be corrected, but the method may ultimately be able to give accurate un-biased results when there is a high galaxy density with constant shear. It provides a useful baseline for more optimal methods, and a test-case for estimating biases, though the method is not directly applicable to realistic data. I test stacking methods on the simple toy simulations with constant PSF and shear provided by the GREAT08 project, on which most other existing methods perform significantly more poorly, and briefly discuss generalizations to more realistic cases. In the appendix I discuss a simple analytic galaxy population model where stacking gives optimal errors in a perfect ideal case.