Limitations of model-fitting methods for lensing shear estimation

L. M. Voigt, S. L. Bridle

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Gravitational lensing shear has the potential to be the most powerful tool for constraining the nature of dark energy. However, accurate measurement of galaxy shear is crucial and has been shown to be non-trivial by the Shear TEsting Programme. Here, we demonstrate a fundamental limit to the accuracy achievable by model-fitting techniques, if oversimplistic models are used. We show that even if galaxies have elliptical isophotes, model-fitting methods which assume elliptical isophotes can have significant biases if they use the wrong profile. We use noisefree simulations to show that on allowing sufficient flexibility in the profile the biases can be made negligible. This is no longer the case if elliptical isophote models are used to fit galaxies made up of a bulge plus a disc, if these two components have different ellipticities. The limiting accuracy is dependent on the galaxy shape, but we find the most significant biases (~1 per cent of the shear) for simple spiral-like galaxies. The implications for a given cosmic shear survey will depend on the actual distribution of galaxy morphologies in the Universe, taking into account the survey selection function and the point spread function. However, our results suggest that the impact on cosmic shear results from current and near future surveys may be negligible. Meanwhile, these results should encourage the development of existing approaches which are less sensitive to morphology, as well as methods which use priors on galaxy shapes learnt from deep surveys. © 2010 The Authors. Journal compilation. © 2010 RAS.
    Original languageEnglish
    Pages (from-to)458-467
    Number of pages9
    JournalMonthly Notices of the Royal Astronomical Society
    Volume404
    Issue number1
    DOIs
    Publication statusPublished - May 2010

    Keywords

    • Cosmology: Observations
    • Gravitational lensing
    • Methods: Data analysis

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