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Bayesian galaxy shape measurement for weak lensing surveys - III. Application to the Canada-France-Hawaii Telescope Lensing Survey

Abstract:
A likelihood-based method for measuring weak gravitational lensing shear in deep galaxy surveys is described and applied to the Canada-France-Hawaii Telescope (CFHT) Lensing Survey (CFHTLenS). CFHTLenS comprises 154 deg2 of multi-colour optical data from the CFHT Legacy Survey, with lensing measurements being made in the i' band to a depth i' AB < 24.7, for galaxies with signal-to-noise ratio νSN ( 10. The method is based on the lensfit algorithm described in earlier papers, but here we describe a full analysis pipeline that takes into account the properties of real surveys. The method creates pixel-based models of the varying point spread function (PSF) in individual image exposures. It fits PSF-convolved two-component (disc plus bulge) models to measure the ellipticity of each galaxy, with Bayesian marginalization over model nuisance parameters of galaxy position, size, brightness and bulge fraction. The method allows optimal joint measurement of multiple, dithered image exposures, taking into account imaging distortion and the alignment of the multiple measurements. We discuss the effects of noise bias on the likelihood distribution of galaxy ellipticity. Two sets of image simulations that mirror the observed properties of CFHTLenS have been created to establish the method's accuracy and to derive an empirical correction for the effects of noise bias. © 2013 T he Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1093/mnras/sts454

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Institution:
University of Oxford
Division:
MPLS
Department:
Physics
Sub department:
Astrophysics
Role:
Author


Publisher:
Oxford University Press
Journal:
Monthly Notices of the Royal Astronomical Society More from this journal
Volume:
429
Issue:
4
Pages:
2858-2880
Publication date:
2013-01-14
Acceptance date:
2012-11-20
DOI:
EISSN:
1365-2966
ISSN:
0035-8711


Keywords:
Pubs id:
pubs:357346
UUID:
uuid:73708f35-287e-4408-98a2-266363ff5649
Local pid:
pubs:357346
Source identifiers:
357346
Deposit date:
2017-08-03

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