Rainbow Cosmic Shear: Optimisation of Tomographic Bins
  In this paper we address the problem of finding optimal cosmic shear
tomographic bins. We generalise the definition of a cosmic shear tomographic
bin to be a set of commonly labelled voxels in photometric colour space; rather
than bins defined directly in redshift. We explore this approach by using a
self-organising map to define the multi-dimensional colour space, and a we
define a 'label space' of connected regions on the self-organising map using
overlapping elliptical disks. This allows us to then find optimal labelling
schemes by searching the label space. We use a metric that is the
signal-to-noise ratio of a dark energy equation of state measurement, and in
this case we find that for up to five tomographic bins the optimal colour-space
labelling is an approximation of an equally-spaced binning in redshift; that is
in all cases the best configuration. We also show that such a redefinition is
more robust to photometric redshift outliers than a standard tomographic bin
selection.
