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

By L. Miller, Catherine Heymans, Thomas D. Kitching, L. Waerbeke, T. Erben, H. Hildebrandt, H. Hoekstra, Y. Mellier, B. Rowe, Jean Coupon and 10 more. 2012

328 citations recorded by the source corpus.

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 deg^2 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 ν_(SN) ≳ 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.

Subfields: Astronomy and Astrophysics, Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9510aab1f37e443018f3c564f317775665bdb8d3

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Astronomy and Astrophysics

  1. Rachel Mandelbaum
  2. H. Hoekstra
  3. Risa Wechsler
  4. F. V. D. Bosch
  5. Catherine Heymans
  6. Andrey V. Kravtsov
  7. Simon D. M. White
  8. L. Hernquist

In Atomic and Molecular Physics, and Optics

  1. C. Hirata
  2. S. More
  3. H. Hoekstra
  4. Sarah Bridle
  5. Rachel Mandelbaum
  6. Y. Mellier
  7. Alexandre Refregier
  8. Adam Amara

In Computer Vision and Pattern Recognition

  1. Michael Hirsch
  2. M. Cacciato
  3. Ofer Lahav
  4. A. Leonard
  5. M. White
  6. Jean-Luc Starck
  7. S. Pires
  8. F. Barbera

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