On Weak Lensing Shape Noise

One of the most powerful techniques to study the dark sector of the Universe is weak gravitational lensing. In practice, to infer the reduced shear, weak lensing measures galaxy shapes, which are the consequence of both the intrinsic ellipticity of the sources and of the integrated gravitational len...

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Bibliographic Details
Published inarXiv.org
Main Authors Sami-Matias Niemi, Kitching, Thomas, Cropper, Mark
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 16.09.2015
Subjects
Bayesian analysis
Ellipticity
Galaxies
Gravitational effects
Gravitational lenses
Noise
Noise reduction
Photometry
Physics - Cosmology and Nongalactic Astrophysics
Physics - Instrumentation and Methods for Astrophysics
Power spectra
Universe
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Summary:One of the most powerful techniques to study the dark sector of the Universe is weak gravitational lensing. In practice, to infer the reduced shear, weak lensing measures galaxy shapes, which are the consequence of both the intrinsic ellipticity of the sources and of the integrated gravitational lensing effect along the line of sight. Hence, a very large number of galaxies is required in order to average over their individual properties and to isolate the weak lensing cosmic shear signal. If this `shape noise' can be reduced, significant advances in the power of a weak lensing surveys can be expected. This paper describes a general method for extracting the probability distributions of parameters from catalogues of data using Voronoi cells, which has several applications, and has synergies with Bayesian hierarchical modelling approaches. This allows us to construct a probability distribution for the variance of the intrinsic ellipticity as a function of galaxy property using only photometric data, allowing a reduction of shape noise. As a proof of concept the method is applied to the CFHTLenS survey data. We use this approach to investigate trends of galaxy properties in the data and apply this to the case of weak lensing power spectra.
ISSN:2331-8422
DOI:10.48550/arxiv.1509.05058