Gravitational Lensing Accuracy Testing 2010 (GREAT10) Challenge Handbook

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.

Published in at http://dx.doi.org/10.1214/11-AOAS484 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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• Image Analysis for Cosmology: Results from the GREAT10 Galaxy Challenge
• IM3SHAPE: A maximum-likelihood galaxy shear measurement code for cosmic gravitational lensing
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• Galaxy shape measurement with convolutional neural networks
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• Hopfield Neural Network deconvolution for weak lensing measurement
• Moment-Based Ellipticity Measurement as a Statistical Parameter Estimation Problem
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• Shear measurement bias II: a fast machine learning calibration method
• Reconstruction of Complex-Valued Fractional Brownian Motion Fields Based on Compressive Sampling and Its Application to PSF Interpolation in Weak Lensing Survey