  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.
