The high sensitivity of the new generation of radio telescopes such as the Square Kilometre Array (SKA) will allow cosmological weak lensing measurements at radio wavelengths that are competitive with optical surveys. We present an adaptation to radio data of lensfit, a method for galaxy shape measurement originally developed and used for optical weak lensing surveys. This likelihood method uses an analytical galaxy model and makes a Bayesian marginalisation of the likelihood over uninteresting parameters. It has the feature of working directly in the visibility domain, which is the natural approach to adopt with radio interferometer data, avoiding systematics introduced by the imaging process. As a proof of concept, we provide results for visibility simulations of individual galaxies with flux density S >= 10 muJy at the phase centre of the proposed SKA1-MID baseline configuration, adopting 12 frequency channels in the band 950 - 1190 MHz. Weak lensing shear measurements from a population of galaxies with realistic flux and scalelength distributions are obtained after natural gridding of the raw visibilities. Shear measurements are expected to be affected by "noise bias": we estimate the bias in the method as a function of signal-to-noise ratio (SNR). We obtain additive and multiplicative bias values that are comparable to SKA1 requirements for SNR > 18 and SNR > 30, respectively. The multiplicative bias for SNR > 10 is comparable to that found in ground-based optical surveys such as CFHTLenS, and we anticipate that similar shear measurement calibration strategies to those used for optical surveys may be used to good effect in the analysis of SKA radio interferometer data.
Slay the Shear: A Unified Statistical Framework for Weak Gravitational Lensing Shear Estimation
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Brookhaven National Laboratory（布鲁克海文国家实验室）；National Center for Supercomputing Applications, University of Illinois Urbana-Champaign（美国国家超级计算应用中心）；Center for Artificial Intelligence Innovation, University of Illinois Urbana-Champaign（人工智能创新中心）
