Fourier Power Function Shapelets (FPFS) Shear Estimator: Performance On Image Simulations We reinterpret the shear estimator developed by Zhang & Komatsu (2011) within the framework of Shapelets and suggest the Fourier Power Function Shapelets (FPFS) shear estimator. Four shapelet modes are calculated from the ability perform of each galaxy’s Fourier transform after deconvolving the purpose Spread Function (PSF) in Fourier area. We propose a novel normalization scheme to assemble dimensionless ellipticity and its corresponding shear responsivity utilizing these shapelet modes. Shear is measured in a conventional method by averaging the ellipticities and responsivities over a big ensemble of galaxies. With the introduction and tuning of a weighting parameter, noise bias is diminished under one p.c of the shear sign. We additionally present an iterative technique to scale back choice bias. The FPFS estimator is developed with none assumption on galaxy morphology, nor any approximation for PSF correction. Moreover, our methodology does not rely on heavy image manipulations nor sophisticated statistical procedures. We check the FPFS shear estimator utilizing several HSC-like picture simulations and the primary outcomes are listed as follows. For extra practical simulations which additionally contain blended galaxies, the blended galaxies are deblended by the primary era HSC deblender earlier than shear measurement. The blending bias is calibrated by picture simulations. Finally, we test the consistency and stability of this calibration. Light from background galaxies is deflected by the inhomogeneous foreground density distributions along the line-of-sight. As a consequence, the images of background galaxies are barely but coherently distorted. Such phenomenon is generally known as weak lensing.