We apply the Fourier Power Function Shapelets (FPFS) shear estimator to the first-year data of the Hyper Suprime-Cam survey to construct a shape catalog. The FPFS shear estimator has been demonstrated to have a multiplicative bias less than 1% in the absence of blending, regardless of complexities of galaxy shapes, smears of point spread functions (PSFs), and contamination from noise. The blending bias is calibrated with realistic image simulations, which include the impact of neighboring objects, using the COSMOS Hubble Space Telescope images. Here we carefully test the influence of PSF model residual on the FPFS shear estimation and the uncertainties in the shear calibration. Internal null tests are conducted to characterize potential systematics in the FPFS shape catalog, and the results are compared with those measured using a catalog where the shapes were estimated using the re-Gaussianization algorithms. Furthermore, we compare various weak-lensing measurements between the FPFS shape catalog and the re-Gaussianization shape catalog and conclude that the weak-lensing measurements between these two shape catalogs are consistent with each other within the statistical uncertainty.

我们将傅里叶幂函数Shapelets（FPFS）剪切估计器应用于Hyper Suprime-Cam调查的第一年数据，以构建形状目录。事实证明，在不进行混合的情况下，FPFS剪切估算器的乘法偏差小于1％，无论星系形状的复杂性，点扩散函数（PSF）的拖尾以及噪声的污染。使用COSMOS哈勃太空望远镜图像，通过逼真的图像模拟（包括相邻对象的影响）校准混合偏差。在这里，我们仔细测试了PSF模型残差对FPFS剪切估计和剪切校准不确定性的影响。进行内部无效测试以表征FPFS形状目录中的潜在系统零件，然后将结果与使用目录进行测量的结果进行比较，该目录使用重新高斯化算法估算形状。此外，我们比较了FPFS形状目录和重新高斯化形状目录之间的各种弱透镜测量结果，并得出结论，这两个形状目录之间的弱透镜测量在统计不确定性内彼此一致。