Journal Article

Noise bias in weak lensing shape measurements

Alexandre Refregier, Tomasz Kacprzak, Adam Amara, Sarah Bridle and Barnaby Rowe

in Monthly Notices of the Royal Astronomical Society

Published on behalf of The Royal Astronomical Society

Volume 425, issue 3, pages 1951-1957
Published in print September 2012 | ISSN: 0035-8711
Published online September 2012 | e-ISSN: 1365-2966 | DOI: http://dx.doi.org/10.1111/j.1365-2966.2012.21483.x
Noise bias in weak lensing shape measurements

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Abstract

Weak lensing experiments are a powerful probe into cosmology through their measurement of the mass distribution of the universe. A challenge for this technique is to control systematic errors that occur when measuring the shapes of distant galaxies. In this paper, we investigate noise bias, a systematic error that arises from second-order noise terms in the shape measurement process. We first derive analytical expressions for the bias of general maximum-likelihood estimators in the presence of additive noise. We then find analytical expressions for a simplified toy model in which galaxies are modelled and fitted with a Gaussian with its size as a single free parameter. Even for this very simple case we find a significant effect. We also extend our analysis to a more realistic six-parameter elliptical Gaussian model. We find that the noise bias is generically of the order of the inverse-squared signal-to-noise ratio (SNR) of the galaxies and is thus of the order of a percent for galaxies of SNR 10, i.e. comparable to the weak lensing shear signal. This is nearly two orders of magnitude greater than the systematic requirements for future all-sky weak lensing surveys. We discuss possible ways to circumvent this effect, including a calibration method using simulations discussed in an associated paper.

Keywords: gravitational lensing: weak; methods: statistical; techniques: image processing; cosmology: observations; dark energy; dark matter

Journal Article.  4989 words.  Illustrated.

Subjects: Astronomy and Astrophysics

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