Bayesian hierarchical modelling of weak lensing - the golden goal
File(s)
Published version
Author(s)
Type
Conference Paper
Abstract
To accomplish correct Bayesian inference from weak lensing shear data
requires a complete statistical description of the data. The natural framework
to do this is a Bayesian Hierarchical Model, which divides the chain of
reasoning into component steps. Starting with a catalogue of shear estimates in
tomographic bins, we build a model that allows us to sample simultaneously from
the the underlying tomographic shear fields and the relevant power spectra
(E-mode, B-mode, and E-B, for auto- and cross-power spectra). The procedure
deals easily with masked data and intrinsic alignments. Using Gibbs sampling
and messenger fields, we show with simulated data that the large (over
67000-)dimensional parameter space can be efficiently sampled and the full
joint posterior probability density function for the parameters can feasibly be
obtained. The method correctly recovers the underlying shear fields and all of
the power spectra, including at levels well below the shot noise.
requires a complete statistical description of the data. The natural framework
to do this is a Bayesian Hierarchical Model, which divides the chain of
reasoning into component steps. Starting with a catalogue of shear estimates in
tomographic bins, we build a model that allows us to sample simultaneously from
the the underlying tomographic shear fields and the relevant power spectra
(E-mode, B-mode, and E-B, for auto- and cross-power spectra). The procedure
deals easily with masked data and intrinsic alignments. Using Gibbs sampling
and messenger fields, we show with simulated data that the large (over
67000-)dimensional parameter space can be efficiently sampled and the full
joint posterior probability density function for the parameters can feasibly be
obtained. The method correctly recovers the underlying shear fields and all of
the power spectra, including at levels well below the shot noise.
Date Issued
2017-12-01
Date Acceptance
2015-07-12
Citation
Proceedings of the MG14 Meeting on General Relativity, 2017, pp.3005-3010
ISBN
978-981-3226-59-3
Publisher
World Scientific
Start Page
3005
End Page
3010
Journal / Book Title
Proceedings of the MG14 Meeting on General Relativity
Copyright Statement
© The Editors. This is an Open Access proceedings volume published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited.
Sponsor
Imperial College Trust
Identifier
http://arxiv.org/abs/1602.05345v1
Grant Number
NA
Source
MG14 Meeting on General Relativity
Subjects
astro-ph.CO
astro-ph.CO
Notes
To appear in the proceedings of the Marcel Grossmann Meeting XIV
Publication Status
Published
Start Date
2015-07-12
Finish Date
2015-07-18
Coverage Spatial
Rome, Italy