Astronomy and Astrophysics – Astrophysics – Galaxy Astrophysics
Scientific paper
2009-01-06
MNRAS 398, 471-476 (2009)
Astronomy and Astrophysics
Astrophysics
Galaxy Astrophysics
7 pages, 1 figure. Updated to match MNRAS-accepted version; added appendix describing an analytic galaxy population model
Scientific paper
10.1111/j.1365-2966.2009.15161.x
Statistics of the weak lensing of galaxies can be used to constrain cosmology if the galaxy shear can be estimated accurately. In general this requires accurate modelling of unlensed galaxy shapes and the point spread function (PSF). I discuss suboptimal but potentially robust methods for estimating galaxy shear by stacking images such that the stacked image distribution is closely Gaussian by the central limit theorem. The shear can then be determined by radial fitting, requiring only an accurate model of the PSF rather than also needing to model each galaxy accurately. When noise is significant asymmetric errors in the centroid must be corrected, but the method may ultimately be able to give accurate un-biased results when there is a high galaxy density with constant shear. It provides a useful baseline for more optimal methods, and a test-case for estimating biases, though the method is not directly applicable to realistic data. I test stacking methods on the simple toy simulations with constant PSF and shear provided by the GREAT08 project, on which most other existing methods perform significantly more poorly, and briefly discuss generalizations to more realistic cases. In the appendix I discuss a simple analytic galaxy population model where stacking gives optimal errors in a perfect ideal case.
No associations
LandOfFree
Galaxy shear estimation from stacked images does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Galaxy shear estimation from stacked images, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Galaxy shear estimation from stacked images will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-632586