Gravitational Lensing Accuracy Testing 2010 (GREAT10) Challenge Handbook

TL;DR

GREAT10 is a public challenge designed to improve algorithms for analyzing astronomical images affected by variable distortions, noise, and convolution, to better prepare for future large-scale surveys.

Contribution

It introduces variable fields into the challenge, including galaxy shape measurement and convolution kernel reconstruction, advancing the realism and complexity of astronomical image analysis tasks.

Findings

Enhanced algorithms for galaxy shape measurement in variable conditions

Improved methods for reconstructing variable convolution kernels

Benchmarking of analysis techniques in realistic simulated environments

Abstract

GRavitational lEnsing Accuracy Testing 2010 (GREAT10) is a public image analysis challenge aimed at the development of algorithms to analyze astronomical images. Specifically, the challenge is to measure varying image distortions in the presence of a variable convolution kernel, pixelization and noise. This is the second in a series of challenges set to the astronomy, computer science and statistics communities, providing a structured environment in which methods can be improved and tested in preparation for planned astronomical surveys. GREAT10 extends upon previous work by introducing variable fields into the challenge. The "Galaxy Challenge" involves the precise measurement of galaxy shape distortions, quantified locally by two parameters called shear, in the presence of a known convolution kernel. Crucially, the convolution kernel and the simulated gravitational lensing shape distortion both now vary as a function of position within the images, as is the case for real data. In addition, we introduce the "Star Challenge" that concerns the reconstruction of a variable convolution kernel, similar to that in a typical astronomical observation. This document details the GREAT10 Challenge for potential participants. Continually updated information is also available from http://www.greatchallenges.info.