PMO OpenIR  > 星系宇宙学和暗能量研究团组
The point spread function reconstruction by using Moffatlets - I
Li, Bai-Shun; Li, Guo-Liang; Cheng, Jun; Peterson, John; Cui, Wei
2016
Source PublicationRESEARCH IN ASTRONOMY AND ASTROPHYSICS
Volume16Issue:9Pages:139
AbstractShear 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.
Document Type期刊论文
Identifierhttp://libir.pmo.ac.cn/handle/332002/16887
Collection星系宇宙学和暗能量研究团组
Recommended Citation
GB/T 7714
Li, Bai-Shun,Li, Guo-Liang,Cheng, Jun,et al. The point spread function reconstruction by using Moffatlets - I[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2016,16(9):139.
APA Li, Bai-Shun,Li, Guo-Liang,Cheng, Jun,Peterson, John,&Cui, Wei.(2016).The point spread function reconstruction by using Moffatlets - I.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,16(9),139.
MLA Li, Bai-Shun,et al."The point spread function reconstruction by using Moffatlets - I".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 16.9(2016):139.
Files in This Item:
File Name/Size DocType Version Access License
2016172.pdf(1217KB)期刊论文作者接受稿开放获取Apache LicenseView Application Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Li, Bai-Shun]'s Articles
[Li, Guo-Liang]'s Articles
[Cheng, Jun]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li, Bai-Shun]'s Articles
[Li, Guo-Liang]'s Articles
[Cheng, Jun]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li, Bai-Shun]'s Articles
[Li, Guo-Liang]'s Articles
[Cheng, Jun]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 2016172.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.