Noise bias in weak lensing shape measurements

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.

[1]  T. Broadhurst,et al.  A Method for Weak Lensing Observations , 1994, astro-ph/9411005.

[2]  F. Valdes,et al.  Detection of systematic gravitational lens galaxy image alignments - Mapping dark matter in galaxy clusters , 1990 .

[3]  Adam Amara,et al.  Optimal Surveys for Weak Lensing Tomography , 2006, astro-ph/0610127.

[4]  G. M. Bernstein,et al.  Shapes and Shears, Stars and Smears: Optimal Measurements for Weak Lensing , 2001 .

[5]  Alexandre Refregier,et al.  Shapelets — II. A method for weak lensing measurements , 2003 .

[6]  B. Rowe Improving PSF modelling for weak gravitational lensing using new methods in model selection , 2009, 0904.3056.

[7]  H. Cramér Mathematical methods of statistics , 1947 .

[8]  HighWire Press Philosophical transactions of the Royal Society of London. Series A, Containing papers of a mathematical or physical character , 1896 .

[9]  Jean-Paul Kneib,et al.  BAYESIAN GALAXY SHAPE ESTIMATION , 2002 .

[10]  R. Fisher,et al.  On the Mathematical Foundations of Theoretical Statistics , 1922 .

[11]  Nick Kaiser A New Shear Estimator for Weak-Lensing Observations , 2000 .

[12]  M. Bartelmann,et al.  Limitations on shapelet-based weak-lensing measurements , 2009, 0906.5092.

[13]  H. Hoekstra,et al.  Weak Gravitational Lensing and Its Cosmological Applications , 2008, 0805.0139.

[14]  T. Kitching,et al.  Bayesian galaxy shape measurement for weak lensing surveys – I. Methodology and a fast-fitting algorithm , 2007, 0708.2340.

[15]  H. Hoekstra,et al.  The Shear Testing Programme – I. Weak lensing analysis of simulated ground-based observations , 2005, astro-ph/0506112.

[16]  R. Ellis,et al.  The Shear TEsting Programme 2: Factors affecting high precision weak lensing analyses , 2006, astro-ph/0608643.

[17]  S. Paulin-Henriksson,et al.  Optimal PSF modelling for weak lensing : complexity and sparsity , 2009, 0901.3557.

[18]  Carl E. Rasmussen,et al.  Max – Planck – Institut f ür biologische Kybernetik Max Planck Institute for Biological Cybernetics Technical Report No . 136 Approximate Inference for Robust Gaussian Process Regression , 2005 .

[19]  A. Lewis Galaxy shear estimation from stacked images , 2009, 0901.0649.

[20]  Y. Mellier,et al.  First Detection of a Gravitational Weak Shear at the Periphery of CL 0024+1654 , 1994 .

[21]  P. Natarajan The shapes of galaxies and their dark halos : New Haven, Connecticut, USA, 28-30 May 2001 , 2002 .

[22]  Alexander S. Szalay,et al.  Galaxy–galaxy weak lensing in the Sloan Digital Sky Survey: intrinsic alignments and shear calibration errors , 2004 .

[23]  S. Bridle,et al.  Limitations of model-fitting methods for lensing shear estimation , 2009, 0905.4801.

[24]  A. Réfrégier Weak Gravitational Lensing by Large-Scale Structure , 2003, astro-ph/0307212.

[25]  Gary M. Bernstein,et al.  Shape measurement biases from underfitting and ellipticity gradients , 2010, 1001.2333.

[26]  Calyampudi R. Rao,et al.  Linear statistical inference and its applications , 1965 .

[27]  A. Amara,et al.  Point spread function calibration requirements for dark energy from cosmic shear , 2007, 0711.4886.