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research article

The Third Gravitational Lensing Accuracy Testing (Great3) Challenge Handbook

Mandelbaum, Rachel
•
Rowe, Barnaby
•
Bosch, James
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2014
The Astrophysical Journal: Supplement Series

The GRavitational lEnsing Accuracy Testing 3 (GREAT3) challenge is the third in a series of image analysis challenges, with a goal of testing and facilitating the development of methods for analyzing astronomical images that will be used to measure weak gravitational lensing. This measurement requires extremely precise estimation of very small galaxy shape distortions, in the presence of far larger intrinsic galaxy shapes and distortions due to the blurring kernel caused by the atmosphere, telescope optics, and instrumental effects. The GREAT3 challenge is posed to the astronomy, machine learning, and statistics communities, and includes tests of three specific effects that are of immediate relevance to upcoming weak lensing surveys, two of which have never been tested in a community challenge before. These effects include many novel aspects including realistically complex galaxy models based on high-resolution imaging from space; a spatially varying, physically motivated blurring kernel; and a combination of multiple different exposures. To facilitate entry by people new to the field, and for use as a diagnostic tool, the simulation software for the challenge is publicly available, though the exact parameters used for the challenge are blinded. Sample scripts to analyze the challenge data using existing methods will also be provided.

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Type
research article
DOI
10.1088/0067-0049/212/1/5
Web of Science ID

WOS:000339232600005

Author(s)
Mandelbaum, Rachel
Rowe, Barnaby
Bosch, James
Chang, Chihway
Courbin, Frederic  
Gill, Mandeep
Jarvis, Mike
Kannawadi, Arun
Kacprzak, Tomasz
Lackner, Claire
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Date Issued

2014

Publisher

University of Chicago Press

Published in
The Astrophysical Journal: Supplement Series
Volume

212

Issue

1

Start page

5

Subjects

gravitational lensing: weak

•

methods: data analysis

•

methods: statistical

•

techniques: image processing

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LASTRO  
Available on Infoscience
August 29, 2014
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/106545
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