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Spectral Stacking: Unbiased Shear Estimation for Weak Gravitational Lensing

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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/persons83805

Bethge,  M
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Hosseini, R., & Bethge, M.(2009). Spectral Stacking: Unbiased Shear Estimation for Weak Gravitational Lensing (186).


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C296-7
Abstract
We present a new method for the estimation of shear in gravitational lensing from a set of galaxy images with unknown distribution of shapes. Common procedures first compute an estimate of some characteristic feature for each individual galaxy and then average over these. The average can be used to estimate the shear as it becomes independent of the individual galaxy shapes with increasing number of images. A common problem of the previous methods is that the estimators of the features are biased. Here we introduce ``it spectral stacking‘‘ which uses the power spectrum as a characteristic feature of the individual galaxies. If the galaxy images are contaminated by Poisson noise, an unbiased estimator of the power spectrum exists which is used in the analysis. Furthermore, the power spectrum is independent of the location of the individual galaxy centers provided the smoothed galaxy intensities decay sufficiently fast. No further assumptions are necessary. The alg orithm won the main contest of the Great08 challenge.