Results of the GREAT08 Challenge: An image analysis competition for cosmological lensing

TL;DR

The paper addresses the weak gravitational lensing shear inference problem under realistic PSF and noise by presenting GREAT08, a blind, cross-disciplinary challenge aimed at reaching the precision needs of future cosmic shear surveys. It details two simulated regimes, LowNoise and RealNoise, each with Known and Blind variants, and introduces a figure of merit $Q$ (and variant $Q_l$) together with multiplicative/additive bias diagnostics $m$ and $c$ to evaluate performance across diverse parameter space. The results reveal regime-dependent strengths across many methods, with significant gains from stacking strategies and PSF-centered statistics that reduce reliance on detailed galaxy modelling, and identify successful methods that are robust to PSF ellipticity and noise variations. The study demonstrates rapid methodological progress, public code release, and a roadmap for GREAT10 to incorporate spatial variation and kernel estimation, advancing the practical prospects of precision cosmic shear analyses.

Abstract

We present the results of the GREAT08 Challenge, a blind analysis challenge to infer weak gravitational lensing shear distortions from images. The primary goal was to stimulate new ideas by presenting the problem to researchers outside the shear measurement community. Six GREAT08 Team methods were presented at the launch of the Challenge and five additional groups submitted results during the 6 month competition. Participants analyzed 30 million simulated galaxies with a range in signal to noise ratio, point-spread function ellipticity, galaxy size, and galaxy type. The large quantity of simulations allowed shear measurement methods to be assessed at a level of accuracy suitable for currently planned future cosmic shear observations for the first time. Different methods perform well in different parts of simulation parameter space and come close to the target level of accuracy in several of these. A number of fresh ideas have emerged as a result of the Challenge including a re-examination of the process of combining information from different galaxies, which reduces the dependence on realistic galaxy modelling. The image simulations will become increasingly sophisticated in future GREAT challenges, meanwhile the GREAT08 simulations remain as a benchmark for additional developments in shear measurement algorithms.

Figures (10)

• Figure 1: Left: The first galaxy of the first LowNoise_Known FITS image. Right: The first galaxy of the first RealNoise_Known FITS image. The signal is a factor of ten smaller for the RealNoise images than the LowNoise images, making the problem much more challenging.
• Figure 2: Upper panel: Schematic of the galaxy parameters used in LowNoise$\_$Blind. Each realisation corresponds to a different set or FITS image file containing 10,000 galaxies. The schematic looks identical for LowNoise$\_$Known. For RealNoise$\_$Known there are 100 shears per branch in place of 5. The bottom row of boxes represents galaxies with the same properties as the penultimate row of boxes, but rotated by 90 degrees. Lower panel: Schematic of the galaxy parameters used in RealNoise$\_$Blind.
• Figure 3: Illustration of the different routes to a combined shear statistic from multiple galaxies. The lower left route is the traditional approach in which each galaxy image is analysed separately to produce a shear estimate. The upper right route illustrates the "stacking" methods which average some statistic of each image and perform shear estimation on the averaged statistic.
• Figure 4: Our figure of merit Q as a function of galaxy size for LowNoise$\_$Blind.
• Figure 5: Shear measurement figure of merit $Q$ as a function of simulation properties for RealNoise_Blind.