Cosmological constraints on the dark energy equation of state and its evolution

TL;DR

This study introduces a simple, extensible parametrization for the dark energy equation of state $w(a)$ to test for time evolution using a joint analysis of SNIa, LSS, and CMB data. By expressing $w(a)$ with two asymptotic values, $w_0$ and $w_1$, and transition controls $(a_s,q)$, the authors perform a likelihood analysis that yields a best-fit present-day value $w(z=0) \,=\, -1.43$ and a nonzero derivative $dw/dz(z=0) \,=\, 1.0$, though the overall goodness-of-fit does not exceed that of $\ ext{Lambda CDM}$, and rank tests on SNIa data do not demand evolving $w$. The analysis highlights degeneracies in CMB and SN data, shows consistency with prior Taylor-based analyses in certain limits, and emphasizes that the parametrization can be extended (e.g., with skewness or multiple transitions) as data improve. Overall, the work provides a robust framework for probing dark energy dynamics while maintaining stability and interpretability, ready to adapt to future high-precision observations.

Abstract

We have calculated constraints on the evolution of the equation of state of the dark energy, w(z), from a joint analysis of data from the cosmic microwave background, large scale structure and type-Ia supernovae. In order to probe the time-evolution of w we propose a new, simple parametrization of w, which has the advantage of being transparent and simple to extend to more parameters as better data becomes available. Furthermore it is well behaved in all asymptotic limits. Based on this parametrization we find that w(z=0)=-1.43^{+0.16}_{-0.38} and dw/dz(z=0) = 1.0^{+1.0}_{-0.8}. For a constant w we find that -1.34 < w < -0.79 at 95% C.L. Thus, allowing for a time-varying w shifts the best fit present day value of w down. However, even though models with time variation in w yield a lower chi^2 than pure LambdaCDM models, they do not have a better goodness-of-fit. Rank correlation tests on SNI-a data also do not show any need for a time-varying w.

Figures (11)

• Figure 1: $w(a/a_s)$ for different values of $q$. The values shown are $q = 0.5, 1,2,5,10$ in order of increasing steepness.
• Figure 2: Contourplot of the function $F$. For each value of $(w_0,w_1)$, $a_s$ and $q$ have been chosen so that $F$ is as close to $F_0 = 1.61$ as possible.
• Figure 3: 68% (dark) and 95% (light) likelihood contours for WMAP and SDSS data only.
• Figure 4: 68% (dark) and 95% (light) likelihood contours for SNI-a data only.
• Figure 5: 68% (dark) and 95% (light) likelihood contours for WMAP, SDSS and SNI-a data.