Imperial College London

Professor Andrew H Jaffe

Faculty of Natural SciencesDepartment of Physics

Professor of Astrophysics and Cosmology



+44 (0)20 7594 7526a.jaffe Website




Miss Louise Hayward +44 (0)20 7594 7679




1018BBlackett LaboratorySouth Kensington Campus






BibTex format

author = {Heavens, A and Alsing, J and Jaffe, A and Hoffmann, T and Kiessling, A and Wandelt, B},
title = {Bayesian hierarchical modelling of weak lensing - the golden goal},
url = {},
year = {2017}

RIS format (EndNote, RefMan)

AB - To accomplish correct Bayesian inference from weak lensing shear datarequires a complete statistical description of the data. The natural frameworkto do this is a Bayesian Hierarchical Model, which divides the chain ofreasoning into component steps. Starting with a catalogue of shear estimates intomographic bins, we build a model that allows us to sample simultaneously fromthe the underlying tomographic shear fields and the relevant power spectra(E-mode, B-mode, and E-B, for auto- and cross-power spectra). The proceduredeals easily with masked data and intrinsic alignments. Using Gibbs samplingand messenger fields, we show with simulated data that the large (over67000-)dimensional parameter space can be efficiently sampled and the fulljoint posterior probability density function for the parameters can feasibly beobtained. The method correctly recovers the underlying shear fields and all ofthe power spectra, including at levels well below the shot noise.
AU - Heavens,A
AU - Alsing,J
AU - Jaffe,A
AU - Hoffmann,T
AU - Kiessling,A
AU - Wandelt,B
PY - 2017///
TI - Bayesian hierarchical modelling of weak lensing - the golden goal
UR -
UR -
ER -