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Handbook for the GREAT08 Challenge: An image analysis competition for cosmological lensing

Bridle, Sarah and Shawe-Taylor, John and Amara, Adam and Applegate, Douglas and Balan, Sreekumar T. and Bergé, Joel and Bernstein, Gary and Dahle, Hakon and Erben, Thomas and Gill, Mandeep and Heavens, Alan and Heymans, Catherine and High, F. William and Hoekstra, Henk and Jarvis, Mike and Kirk, Donnacha and Kitching, Thomas and Kneib, Jean-Paul and Kuijken, Konrad and Lagatutta, David and Mandelbaum, Rachel and Massey, Richard and Mellier, Yannick and Moghaddam, Baback and Moudden, Yassir and Nakajima, Reiko and Paulin-Henriksson, Stéphane and Pires, Sandrine and Rassat, Anaïs and Refregier, Alexandre and Rhodes, Jason and Schrabback, Tim and Semboloni, Elisabetta and Shmakova, Marina and Van Waerbeke, Ludovic and Witherick, Dugan and Voigt, Lisa and Wittman, David (2009) Handbook for the GREAT08 Challenge: An image analysis competition for cosmological lensing. Annals of Applied Statistics, 3 (1). pp. 6-37. ISSN 1932-6157. https://resolver.caltech.edu/CaltechAUTHORS:20100108-120338051

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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 eects of pixelisation, convolution and noise. The worldwide gravitational lensing community has made signicant 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. Please visit www.great08challenge.info for the latest information.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1214/08-AOAS222DOIArticle
http://projecteuclid.org/euclid.aoas/1239888361PublisherArticle
ORCID:
AuthorORCID
Kneib, Jean-Paul0000-0002-4616-4989
Mandelbaum, Rachel0000-0003-2271-1527
Massey, Richard0000-0002-6085-3780
Rhodes, Jason0000-0002-4485-8549
Wittman, David0000-0002-0813-5888
Additional Information:2010 © Institute of Mathematical Statistics. This project was born from the Shear TEsting Programme and a clinic at the University College London (UCL) Centre for Computational Statistics and Machine Learning (CSML). The GREAT08 Challenge is a Pattern Analysis, Statistical Modelling and Computational Learning (PASCAL) Challenge. PASCAL is a European Network of Excellence under Framework 6. We thank John Bridle, Michiel van de Panne, Michele Sebag, Antony Lewis, Christoph Lampert, Bernhard Schoelkopf, Chris Williams, David MacKay, Maneesh Sahani, David Barber and Nick Kaiser for helpful discussions. SLB acknowledges support from the Royal Society in the form of a University Research Fellowship. CH acknowledges the support of a European Commission Programme 6th framework Marie Curie Outgoing International Fellowship under contract MOIF-CT-2006- 21891. The work was supported in part by the Jet Propulsion Laboratory, which is run by Caltech under a contract from NASA.
Funders:
Funding AgencyGrant Number
Royal SocietyUNSPECIFIED
Marie Curie FellowshipMOIF-CT-2006- 21891
NASAUNSPECIFIED
JPLUNSPECIFIED
CaltechUNSPECIFIED
Subject Keywords:inference; inverse problems; astronomy
Issue or Number:1
Record Number:CaltechAUTHORS:20100108-120338051
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20100108-120338051
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:17111
Collection:CaltechAUTHORS
Deposited By: Joy Painter
Deposited On:12 Jan 2010 18:51
Last Modified:09 Mar 2020 13:19

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