Cosmological model differentiation through weak gravitational lensing

Abstract

We investigate the potential of weak gravitational lensing maps to differentiate between distinct cosmological models, considering cosmic variance due to a limited map extension and the presence of noise. We introduce a measure of the differentiation between two models under a chosen lensing statistics. That enables one to determine in which circumstances (map size and noise level), and for which lensing measures two models can be differentiated at a desired confidence level. As an application, we simulate convergence maps for three cosmological models (SCDM, OCDM, and $\Lambda$CDM), calculate several lensing analyses for them, and compute the differentiation between the models under these analyses. We use first, second, and higher order statistics, including Minkowski functionals, which provide a complete morphological characterization of the lensing maps. We examine for each lensing measure used how noise affects its description of the convergence, and how this affects its ability to differentiate between cosmological models. Our results corroborate to the valuable use of weak gravitational lensing as a cosmological tool.

12 pages, 8 figures. Submitted to MNRAS

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Cited by in corpus (8)

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• Cosmology with Minkowski functionals and moments of the weak lensing convergence field
• Baryon impact on weak lensing peaks and power spectrum: low-bias statistics and self-calibration in future surveys
• Constraining Cosmology with High Convergence Regions in Weak Lensing Surveys
• Effect of Measurement Errors on Predicted Cosmological Constraints from Shear Peak Statistics with LSST
• A comparative study of cosmological constraints from weak lensing using Convolutional Neural Networks
• The impact of spurious shear on cosmological parameter estimates from weak lensing observables