Fourier Power Function Shapelets (FPFS) Shear Estimator: Performance on Image Simulations
Xiangchong Li, Nobuhiko Katayama, Masamune Oguri, Surhud More

TL;DR
The paper introduces the Fourier Power Function Shapelets (FPFS) shear estimator, which accurately measures cosmic shear from galaxy images without heavy assumptions or complex procedures, demonstrating low bias in simulations.
Contribution
The FPFS shear estimator is a novel, model-independent method that effectively reduces noise and bias in shear measurements using Fourier space shapelet modes.
Findings
Multiplicative bias below 1% for isolated galaxies
Bias of approximately -5.7% in blended galaxy simulations
Calibration and stability confirmed through 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…
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