March 2019 • 2019PhRvD..99f3536K
Abstract • In this paper, we address the problem of finding optimal cosmic shear tomographic bins. We generalize the definition of a cosmic shear tomographic bin to be a set of commonly labeled voxels in photometric color space; rather than bins defined directly in redshift. We explore this approach by using a self-organizing map to define the multidimensional color space, and a we define a "label space" of connected regions on the self-organizing map using overlapping elliptical disks. This allows us to then find optimal labeling 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 color-space labeling 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.