Bayesian Galaxy Shape Measurement for Weak Lensing Surveys -I. Methodology and a Fast Fitting Algorithm

TL;DR

This paper introduces a Bayesian model-fitting approach for galaxy shape measurement in weak lensing surveys, emphasizing optimal signal extraction, error estimation, and an efficient algorithm suitable for large datasets.

Contribution

It presents a novel, fast Bayesian fitting algorithm that enables unbiased shear estimation without external calibration, suitable for large-scale weak lensing surveys.

Findings

Algorithm successfully tested on simulated data from STEP

Achieves unbiased shear estimates without external calibration

Reduces computational time for large surveys

Abstract

The principles of measuring the shapes of galaxies by a model-fitting approach are discussed in the context of shape-measurement for surveys of weak gravitational lensing. It is argued that such an approach should be optimal, allowing measurement with maximal signal-to-noise, coupled with estimation of measurement errors. The distinction between likelihood-based and Bayesian methods is discussed. Systematic biases in the Bayesian method may be evaluated as part of the fitting process, and overall such an approach should yield unbiased shear estimation without requiring external calibration from simulations. The principal disadvantage of model-fitting for large surveys is the computational time required, but here an algorithm is presented that enables large surveys to be analysed in feasible computation times. The method and algorithm is tested on simulated galaxies from the Shear TEsting Program (STEP).