Fourier Power Function Shapelets (FPFS) Shear Estimator: Performance on Image Simulations

Abstract

We reinterpret the shear estimator developed by Zhang & Komatsu (2011) within the framework of Shapelets and propose the Fourier Power Function Shapelets (FPFS) shear estimator. Four shapelet modes are calculated from the power function of every galaxy's Fourier transform after deconvolving the Point Spread Function (PSF) in Fourier space. We propose a novel normalization scheme to construct dimensionless ellipticity and its corresponding shear responsivity using these shapelet modes. Shear is measured in a conventional way by averaging the ellipticities and responsivities over a large ensemble of galaxies. With the introduction and tuning of a weighting parameter, noise bias is reduced below one percent of the shear signal. We also provide an iterative method to reduce selection bias. The FPFS estimator is developed without any assumption on galaxy morphology, nor any approximation for PSF correction. Moreover, our method does not rely on heavy image manipulations nor complicated statistical procedures. We test the FPFS shear estimator using several HSC-like image simulations and the main results are listed as follows. (i) For simulations which only contain isolated galaxies, the amplitude of multiplicative bias is below 1 % . (ii) For more realistic simulations which also contain blended galaxies, the blended galaxies are deblended by the first generation HSC deblender before shear measurement. Multiplicative bias of (-5.71 +- 0.31) % is found. The blending bias is calibrated by image simulations. Finally, we test the consistency and stability of this calibration.