Bayesian Galaxy Shape Measurement for Weak Lensing Surveys -II. Application to Simulations

In this paper we extend the Bayesian model fitting shape measurement method presented in Miller et al. (2007) and use the method to estimate the shear from the Shear TEsting Programme simulations (STEP). The method uses a fast model fitting algorithm which uses realistic galaxy profiles and analytically marginalises over the position and amplitude of the model by doing the model fitting in Fourier space. This is used to find the full posterior probability in ellipticity. The shear is then estimated in a Bayesian way from this posterior probability surface. The Bayesian estimation allows measurement bias arising from the presence of random noise to be removed. In this paper we introduce an iterative algorithm that can be used to estimate the intrinsic ellipticity prior and show that this is accurate and stable. We present results using the STEP parameterisation which relates the input shear γ to the estimated shear γ by introducing a bias m and an offset c: γ − γ = mγ + c. By using the method to estimate the shear from the STEP1 simulations we find the method to have a shear bias ofm ∼ 5×10 and a variation in shear offset with PSF type of σc ∼ 2 × 10 . These values are smaller than for any method presented in the STEP1 publication that behaves linearly with shear. Using the method to estimate the shear from the STEP2 simulations we find than the shear bias and offset are m ∼ 2 × 10 and c ∼ −7 × 10 respectively. In addition we find that the bias and offset are stable to changes in magnitude and size of the galaxies. Such biases should yield any cosmological constraints from future weak lensing surveys robust to systematic effects in shape measurement. Finally we present an alternative to the STEP parameterisation by using a Quality factor that relates the intrinsic shear variance in a simulation to the variance in shear that is measured and show that the method presented has an average of Q > ∼ 100 which is at least a factor of 10 times better than other shape measurement methods.