Evolution of Dark Energy Reconstructed from the Latest Observations

By Yuting Wang, Levon Pogosian, Gong-Bo Zhao, Alex Zucca. Astrophysical Journal, 2018

133 citations recorded by the source corpus.

We reconstruct the evolution of the dark energy (DE) density using a nonparametric Bayesian approach from a combination of the latest observational data. We caution against parameterizing DE in terms of its equation of state as it can be singular in modified gravity models, and using it introduces a bias preventing negative effective DE densities. We find a 3.7σ preference for an evolving effective DE density with interesting features. For example, it oscillates around the Λ cold dark matter prediction at z ≲ 0.7, and could be negative at z ≳ 2.3; DE can be pressure-less at multiple redshifts, and a short period of cosmic deceleration is allowed at 0.1 ≲ z ≲ 0.2. We perform the reconstruction for several choices of the prior, as well as a evidence-weighted reconstruction. We find that some of the dynamical features, such as the oscillatory behavior of the DE density, are supported by the Bayesian evidence, which is a first detection of a dynamical DE with a positive Bayesian evidence. The evidence-weighted reconstruction prefers a dynamical DE at a (2.5 ± 0.06)σ significance level.

Subfields: Astronomy and Astrophysics, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/7ff2d9ccd0c4b7f92d1ae0a458be7a13c290d331

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In Astronomy and Astrophysics

In Nuclear and High Energy Physics

Citations in the corpus

DESI 2024: reconstructing dark energy using crossing statistics with DESI DR1 BAO data (2024, background): “At higher redshift, z ≳ 1, on the other hand, the trend of the reconstructed form of the dark energy density allows models with emergent dark energy behaviour [52, 53, 108, 109].”