# Rainbow Cosmic Shear: Optimisation of Tomographic Bins

## TL;DR

This paper proposes a novel method for optimizing cosmic shear tomographic bins using a self-organising map in colour space, improving robustness to redshift outliers and matching redshift binning.

## Contribution

It introduces a new approach to define tomographic bins in colour space with a self-organising map, optimizing binning for dark energy measurements.

## Key findings

- Optimal colour-space binning approximates equally-spaced redshift bins.
- Method is more robust to photometric redshift outliers.
- Optimal binning improves dark energy measurement signal-to-noise ratio.

## Abstract

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.