Cosmic Shear Tomography and Efficient Data Compression using COSEBIs

TL;DR

This paper evaluates the effectiveness of COSEBIs, a statistical measure for cosmic shear data, demonstrating that a small number of modes efficiently captures all relevant information, simplifying analysis and data compression.

Contribution

The study extends COSEBIs to shear tomography and analyzes its efficiency in high-dimensional parameter spaces using Fisher analysis.

Findings

A small number of COSEBIs modes saturates maximum information.

Logarithmic COSEBIs require fewer modes than linear ones.

COSEBIs modes simplify covariance estimation and data analysis.

Abstract

Context. Gravitational lensing is one of the leading tools in understanding the dark side of the Universe. The need for accurate, efficient and effective methods which are able to extract this information along with other cosmological parameters from cosmic shear data is ever growing. COSEBIs, Complete Orthogonal Sets of E-/B-Integrals, is a recently developed statistical measure that encompasses the complete E-/B-mode separable information contained in the shear correlation functions measured on a finite angular range. Aims. The aim of the present work is to test the properties of this newly developed statistics for a higher-dimensional parameter space and to generalize and test it for shear tomography. Methods. We use Fisher analysis to study the effectiveness of COSEBIs. We show our results in terms of figure-of-merit quantities, based on Fisher matrices. Results. We find that a relatively small number of COSEBIs modes is always enough to saturate to the maximum information level. This number is always smaller for 'logarithmic COSEBIs' than for 'linear COSEBIs', and also depends on the number of redshift bins, the number and choice of cosmological parameters, as well as the survey characteristics. Conclusions. COSEBIs provide a very compact way of analyzing cosmic shear data, i.e., all the E-/B-mode separable second-order statistical information in the data is reduced to a small number of COSEBIs modes. Furthermore, with this method the arbitrariness in data binning is no longer an issue since the COSEBIs modes are discrete. Finally, the small number of modes also implies that covariances, and their inverse, are much more conveniently obtainable, e.g., from numerical simulations, than for the shear correlation functions themselves.