PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Gravitational Lensing Accuracy Testing 2010 (GREAT10) Challenge Handbook
Thomas Kitching, Adam Amara, Mandeep Gill, Stefan Harmeling, Catherine Heymans, Richard Massey, Barnaby Rowe, Tim Schrabback, Lisa Voigt, Sreekumar Balan, Gary Bernstein, Matthias Bethge, Sarah Bridle, Frederic Courbin, Marc Gentile, Alan Heavens, Michael Hirsch, Reshad Hosseini, Alina Kiessling, Donnacha Kirk, Konrad Kuijken, Rachel Mandelbaum, Baback Moghaddam, Guldariya Nurbaeva, Stephane Paulin-Henriksson, Anais Rassat, Jason Rhodes, Bernhard Schölkopf, John Shawe-Taylor, Marina Shmakova, Andy Taylor, Malin Velander, Ludovic van Waerbeke, Dugan WItherick and David Wittman
Annals of Applied Statistics 2010.


GRavitational lEnsing Accuracy Testing 2010 (GREAT10) is a public image analysis challenge aimed at the development of algorithms to analyse 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

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EPrint Type:Article
Additional Information:Currently being refereed, minor revisions suggested, publication date likely to be early 2011.
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:7164
Deposited By:Thomas Kitching
Deposited On:07 March 2011