Narrowing Constraints with Type Ia Supernovae: Converging on a Cosmological Constant

TL;DR

This paper uses a model-independent method with supernova, CMB, and BAO data to show that dark energy is consistent with a cosmological constant and constrains its evolution without assuming a specific model.

Contribution

It introduces a parameterization-independent approach to constrain the dark energy equation-of-state using multiple cosmological data sets.

Findings

Dark energy is consistent with a cosmological constant.

Constraints show EOS more negative than -0.2 at 68% confidence.

Results challenge claims of dark energy metamorphosis and extreme dynamical models.

Abstract

We apply a parameterization-independent approach to fitting the dark energy equation-of-state (EOS). Utilizing the latest type Ia supernova data, combined with results from the cosmic microwave background and baryon acoustic oscillations, we find that the dark energy is consistent with a cosmological constant. We establish independent estimates of the evolution of the dark energy EOS by diagonalizing the covariance matrix. We find three independent constraints, which taken together imply that the equation of state is more negative than -0.2 at the 68% confidence level in the redshift range 0<z<1.8, independent of the flat universe assumption. Our estimates argue against previous claims of dark energy ``metamorphosis,'' where the EOS was found to be strongly varying at low redshifts. Our results are inconsistent with extreme models of dynamical dark energy, both in the form of ``freezing'' models where the dark energy EOS begins with a value greater than -0.2 at z > 1.2 and rolls to a value of -1 today, and ``thawing'' models where the EOS is near -1 at high redshifts, but rapidly evolves to values greater than -0.85 at z < 0.2. Finally, we propose a parameterization-independent figure-of-merit, to help assess the ability of future surveys to constrain dark energy. While previous figures-of-merit presume specific dark energy parameterizations, we suggest a binning approach to evaluate dark energy constraints with a minimum number of assumptions.