de.mpg.escidoc.pubman.appbase.FacesBean
Deutsch
 
Hilfe Wegweiser Impressum Kontakt Einloggen
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

Gravitational Lensing Accuracy Testing 2010 (GREAT10) Challenge Handbook

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83954

Amara A, Gill M, Harmeling,  S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83805

Heymans C, Massey R, Rowe B, Schrabback T, Voigt L, Balan S, Bernstein G, Bethge,  M
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83969

Bridle S, Courbin F, Gentile M, Heavens A, Hirsch,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83982

Hosseini,  R
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Kiessling A, Kirk D, Kuijken K, Mandelbaum R, Moghaddam B, Nurbaeva G, Paulin-Henriksson S, Rassat A, Rhodes J, Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Kitching, T., Amara A, Gill M, Harmeling, S., Heymans C, Massey R, Rowe B, Schrabback T, Voigt L, Balan S, Bernstein G, Bethge, M., Bridle S, Courbin F, Gentile M, Heavens A, Hirsch, M., Hosseini, R., Kiessling A, Kirk D, Kuijken K, Mandelbaum R, Moghaddam B, Nurbaeva G, Paulin-Henriksson S, Rassat A, Rhodes J, Schölkopf, B., et al. (2011). Gravitational Lensing Accuracy Testing 2010 (GREAT10) Challenge Handbook. Annals of Applied Statistics, 5(3), 2231-2263. doi:10.1214/11-AOAS484.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-BA20-A
Zusammenfassung
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 www.greatchallenges.info.