Shear measurement bias I: dependencies on methods, simulation parameters and measured parameters

TL;DR

This study investigates how shear measurement biases depend on various simulation and galaxy properties, revealing complex interactions and the importance of multiple parameters for accurate bias modeling.

Contribution

It provides a detailed analysis of shear bias dependencies on input/output parameters, noise, PSF anisotropy, and galaxy morphology using two independent shape estimators.

Findings

Bias depends on input/output parameters, noise, and pixelization.

Bias varies with galaxy orientation, flux, size, and ellipticity.

Coupling of properties affects shear bias complexity.

Abstract

We present a study of the dependencies of shear bias on simulation (input) and measured (output) parameters, noise, point-spread function anisotropy, pixel size, and the model bias coming from two different and independent galaxy shape estimators. We used simulated images from Galsim based on the GREAT3 control-space-constant branch, and we measured shear bias from a model-fitting method (gFIT) and a moment-based method (Kaiser-Squires-Broadhurst). We show the bias dependencies found on input and output parameters for both methods, and we identify the main dependencies and causes. Most of the results are consistent between the two estimators, an interesting result given the differences of the methods. We also find important dependences on orientation and morphology properties such as flux, size, and ellipticity. We show that noise and pixelization play an important role in the bias dependencies on the output properties and galaxy orientation. We show some examples of model bias that produce a bias dependence on the Sersic index n as well as a different shear bias between galaxies consisting of a single Sersic profile and galaxies with a disc and a bulge. We also see an important coupling between several properties on the bias dependences. Because of this, we need to study several measured properties simultaneously in order to properly understand the nature of shear bias. This paper serves as a first step towards a companion paper that describes a machine learning approach to modelling shear bias as a complex function of many observed properties.