Bayesian model selection for dark energy using weak lensing forecasts

TL;DR

This paper evaluates how future weak lensing surveys, like Euclid, can effectively differentiate between dark energy models, including a cosmological constant and dynamical dark energy, using Bayesian model selection.

Contribution

It demonstrates the potential of upcoming all-sky weak lensing surveys to distinguish dark energy models through Fisher forecasts and Bayesian evidence analysis.

Findings

Future surveys can constrain dark energy models effectively.

Bayesian evidence can differentiate between cosmological constant and dynamical dark energy.

Fisher matrix forecasts show high statistical accuracy for model discrimination.

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.