PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Handbook for the GREAT08 Challenge: An image analysis competition for cosmological lensing
S. L. Bridle, John Shawe-Taylor and +29 co-authors from astronomy backgrounds
Annals of Applied Statistics In press. Currently on arXiv Volume 0802.1214, 2008.

Abstract

The GRavitational lEnsing Accuracy Testing 2008 (GREAT08) Challenge focuses on a problem that is of crucial importance for future observations in cosmology. The shapes of distant galaxies can be used to determine the properties of dark energy and the nature of gravity, because light from those galaxies is bent by gravity from the intervening dark matter. The observed galaxy images appear distorted, although only slightly, and their shapes must be precisely disentangled from the effects of pixelisation, convolution and noise. The worldwide gravitational lensing community has made significant progress in techniques to measure these distortions via the Shear TEsting Program (STEP). Via STEP, we have run challenges within our own community, and come to recognise that this particular image analysis problem is ideally matched to experts in statistical inference, inverse problems and computational learning. Thus, in order to continue the progress seen in recent years, we are seeking an infusion of new ideas from these communities. This document details the GREAT08 Challenge for potential participants.

EPrint Type:Article
Additional Information:This is a document from the astrophysics community to the computer science community. It describes the GREAT08 PASCAL Challenge.
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:5218
Deposited By:Sarah Bridle
Deposited On:24 March 2009