Bayesian model selection for dark energy using weak lensing forecasts

Abstract

The next generation of weak lensing probes can place strong constraints on cosmological parameters by measuring the mass distribution and geometry of the low redshift universe. We show that a future all-sky tomographic cosmic shear survey with design properties similar to Euclid can provide the statistical accuracy required to distinguish between different dark energy models. Using a fiducial cosmological model which includes cold dark matter, baryons, massive neutrinos (hot dark matter), a running primordial spectral index and possible spatial curvature as well as dark energy perturbations, we calculate Fisher matrix forecasts for different dynamical dark energy models. Using a Bayesian evidence calculation we show how well a future weak lensing survey could do in distinguishing between a cosmological constant and dynamical dark energy.

12 pages, 4 figures, accepted for publication by MNRAS

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Cited by in corpus (6)

• General Relativity and Cosmology: Unsolved Questions and Future Directions
• Bayesian Evidence and Model Selection
• Breaking the spell of Gaussianity: forecasting with higher order Fisher matrices
• Constraints on features in the inflationary potential from future Euclid data
• Bayesian analysis of running holographic Ricci dark energy
• Dark energy model selection with current and future data