Observational constraints on one-parameter dynamical dark-energy parametrizations and the $H_0$ tension

TL;DR

The paper investigates five one-parameter dynamical dark-energy parametrizations by deriving their background and perturbation evolution and confronting them with Planck 2015 CMB, JLA SNIa, BAO, and cosmic chronometer data. Using CosmoMC, the authors quantify constraints on the DE present value $w_0$ and cosmological parameters, finding a persistent phantom $w_0$ and that inclusion of external data can bring $H_0$ into agreement with local measurements, while $\sigma_8$ remains aligned with Planck. A Bayesian evidence analysis shows ΛCDM is generally preferred, though two of the one-parameter models (II and III) are closest to ΛCDM and competitive with some two-parameter DE models. Overall, one-parameter DE models offer an economical alternative with potential to address the $H_0$ tension, warranting further exploration.

Abstract

The phenomenological parametrizations of dark-energy (DE) equation of state can be very helpful, since they allow for the investigation of its cosmological behavior despite the fact that its underlying theory is unknown. However, although there has been a large amount of research on DE parametrizations which involve two or more free parameters, the one-parameter parametrizations seem to be underestimated. We perform a detailed observational confrontation of five one-parameter DE models, with observational data from cosmic microwave background (CMB), Joint light-curve analysis sample from Supernovae Type Ia observations (JLA), baryon acoustic oscillations (BAO) distance measurements, and cosmic chronometers (CC). We find that all models favor a phantom DE equation of state at present time, while they lead to $H_0$ values in perfect agreement with its direct measurements and therefore they offer an alleviation to the $H_0$-tension. Finally, performing a Bayesian analysis we show that although $Λ$CDM cosmology is still favored, one-parameter DE models have similar or better efficiency in fitting the data comparing to two-parameter DE parametrizations, and thus they deserve a thorough investigation.

Figures (6)

• Figure 1: The evolution of the one-parameter dynamical DE equation-of-state parametrizations (\ref{['model1']})-(\ref{['model5']}) as a function of the scale factor, for $w_0= -0.9$ (left graph) and $w_0 = -1.2$ (right graph).
• Figure 2: Whisker graph with the 68% CL (solid line) and 95% CL (dashed line) regions for the free model parameter $w_0$ of the DE parametrizations (\ref{['model1']})-(\ref{['model5']}), for the combination of datasets considered in this work.
• Figure 3: The CMB TT spectra (left graph) and the matter power spectra (right graph), for Model I of (\ref{['model1']}), namely $w_x(a)=w_0\exp(a-1)$, for various values of the free model parameter $w_0$.
• Figure 4: The 2D contour plots for several combinations of various quantities for Model I of (\ref{['model1']}), namely $w_x(a)=w_0\exp(a-1)$, and the corresponding 1D posterior distributions.
• Figure 5: Comparison between the best fit for the $\Lambda$CDM paradigm and for the Model I of (\ref{['model1']}), namely $w_x(a)=w_0\exp(a-1)$. While the curves are almost indistinguishable in the high multipole range, at large scales Model I can better recover the lower quadrupole of the data.