Rainbow cosmic shear: Optimization of tomographic bins

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...

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Bibliographic Details
Published in:Physical review. D 2019-03, Vol.99 (6), Article 063536
Main Authors: Kitching, Thomas D., Taylor, Peter L., Capak, Peter, Masters, Daniel, Hoekstra, Henk
Format: Article
Language:English
Subjects:
Bins
Color
Dark energy
Disks
Energy measurement
Equations of state
Labeling
Optimization
Outliers (statistics)
Photometry
Rainbows
Red shift
Self organizing maps
Shear
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Summary: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.
ISSN:2470-0010
2470-0029
DOI:10.1103/PhysRevD.99.063536