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, Frédéric 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, David Wittman
September, 2011
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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 …

Matthias Bethge
Matthias Bethge
Professor for Computational Neuroscience and Machine Learning & Director of the Tübingen AI Center

Matthias Bethge is Professor for Computational Neuroscience and Machine Learning at the University of Tübingen and director of the Tübingen AI Center, a joint center between Tübingen University and MPI for Intelligent Systems that is part of the German AI strategy.