Work

The point spread function reconstruction by using Moffatlets — I

Year: 2016

Type: article

Abstract: Shear measurement is a crucial task in current and future weak lensing survey projects. The reconstruction of the point spread function (PSF) is one of the essential steps involved in this process. In this work, we present three different methods, Gaussianlets, Moffatlets and Expectation Maximization Principal Component Analysis (EMPCA), and quantify their efficiency on PSF reconstruction using four sets of simulated Large Synoptic Survey Telescope (LSST) star images. Gaussianlets and Moffatlets are two different sets of basis functions whose profiles are based on Gaussian and Moffat functions respectively. EMPCA is a statistical method performing an iterative procedure to find the principal components (PCs) of an ensemble of star images. Our tests show that: (1) Moffatlets always perform better than Gaussianlets. (2) EMPCA is more compact and flexible, but the noise existing in the PCs will contaminate the size and ellipticity of PSF. By contrast, Moffatlets keep the size and ellipticity very well.

Source: Research in Astronomy and Astrophysics

DOI: 10.1088/1674-4527/16/9/139

Authors: Bai-Shun Li, Guo-Liang Li, Jun Cheng, John Peterson, Wei Cui

Institutions: Chinese Academy of Sciences, Purple Mountain Observatory, University of Chinese Academy of Sciences, Purdue University West Lafayette

Language: English

Topic: Adaptive optics and wavefront sensing

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: bronze

Funders: U.S. Department of Energy, Purdue University, National Natural Science Foundation of China, Chinese Academy of Sciences