pith. sign in

arxiv: astro-ph/0404062 · v1 · pith:BXQAQXJUnew · submitted 2004-04-02 · 🌌 astro-ph · hep-ph

Uncorrelated Estimates of Dark Energy Evolution

classification 🌌 astro-ph hep-ph
keywords energydarkdensityequationstateuncorrelatedanalysesband
0
0 comments X
read the original abstract

Type Ia supernova data have recently become strong enough to enable, for the first time, constraints on the time variation of the dark energy density and its equation of state. Most analyses, however, are using simple two or three-parameter descriptions of the dark energy evolution, since it is well known that allowing more degrees of freedom introduces serious degeneracies. Here we present a method to produce uncorrelated and nearly model-independent band power estimates of the equation of state of dark energy and its density as a function of redshift. We apply the method to recently compiled supernova data. Our results are consistent with the cosmological constant scenario, in agreement with other analyses that use traditional parameterizations, though we find marginal (2-sigma) evidence for w(z) < -1 at z < 0.2. In addition to easy interpretation, uncorrelated, localized band powers allow intuitive and powerful testing of the constancy of either the energy density or equation of state. While we have used relatively coarse redshift binning suitable for the current set of about 150 supernovae, this approach should reach its full potential in the future, when applied to thousands of supernovae found from ground and space, combined with complementary information from other cosmological probes.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Quintom Model Perturbations

    astro-ph.CO 2026-06 unverdicted novelty 4.0

    A two-field quintom model reproduces w0waCDM perturbation features and is mildly favored over it in Bayesian fits to BAO, CMB, and SNIa data.