Weak gravitational shear and flexion with polar shapelets

Massey, R.; Rowe, B.; Refregier, A.; Bacon, D.J.; Bergé, J.

Abstract

We derive expressions, in terms of ‘polar shapelets’, for the image distortion operations associated with weak gravitational lensing. Shear causes galaxy shapes to become elongated, and is sensitive to the second derivative of the projected gravitational potential along their line of sight; flexion bends galaxy shapes into arcs, and is sensitive to the third derivative. Polar shapelets provide a natural representation, in which both shear and flexion transformations are compact. Through this tool, we understand progress in several weak lensing methods. We then exploit various symmetries of shapelets to construct a range of shear estimators with useful properties. Through an analogous investigation, we also explore several flexion estimators. In particular, some of the estimators can be measured simultaneously and independently for every galaxy, and will provide unique checks for systematics in future weak lensing analyses. Using simulated images from the Shear TEsting Programme, we show that we can recover input shears with no significant bias. A complete software package to parametrize astronomical images in terms of polar shapelets, and to perform a full weak lensing analysis, is available on the Internet.

Citation

Massey, R., Rowe, B., Refregier, A., Bacon, D., & Bergé, J. (2007). Weak gravitational shear and flexion with polar shapelets. Monthly Notices of the Royal Astronomical Society, 380(1), 229-245. https://doi.org/10.1111/j.1365-2966.2007.12072.x

• Journal Article Type: Article
• Publication Date: Sep 1, 2007
• Deposit Date: Mar 21, 2013
• Publicly Available Date: Feb 20, 2015
• Journal: Monthly Notices of the Royal Astronomical Society
• Print ISSN: 0035-8711
• Electronic ISSN: 1365-2966
• Publisher: Royal Astronomical Society
• Peer Reviewed: Peer Reviewed
• Volume: 380
• Issue: 1
• Pages: 229-245
• DOI: https://doi.org/10.1111/j.1365-2966.2007.12072.x
• Keywords: Gravitational lensing, Methods: data analysis, Techniques: image processing.