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Probing dynamics of dark energy with latest observations

Yuecheng Zhang, Hanyu Zhang, Dandan Wang, Yanghan Qi, Yuting Wang, Gong-Bo Zhao

TL;DR

This study tests the ΛCDM paradigm by using the $Om(z)$ diagnostic to quantify inter-dataset tension and then reconstructs the dark energy equation of state with two parametrisations of $w(a)$: a polynomial expansion up to $N_p\le4$ and an oscillatory form. It combines diverse cosmological probes (Planck, BAO, Ly$\alpha$, OHD, SNIa, weak lensing) and employs MCMC with Bayesian model selection to assess whether evolving $w$ is favored over a cosmological constant. The current data show tension that can be mildly alleviated by dynamical dark energy, notably an oscillatory $w(z)$ that crosses $-1$, but the Bayesian evidence does not support these extensions over $\Lambda$CDM; forecasts with DESI and EuclidSN suggest that future data could detect dynamics at $\sim7\sigma$ with decisive Bayesian support if the best-fit current model is correct. Overall, the work highlights potential dark energy dynamics as a path to reconcile dataset tensions and underscores the power of forthcoming surveys to decisively test $w(z)$.

Abstract

We examine the validity of the $Λ$CDM model, and probe for the dynamics of dark energy using latest astronomical observations. Using the $Om(z)$ diagnosis, we find that different kinds of observational data are in tension within the $Λ$CDM framework. We then allow for dynamics of dark energy and investigate the constraint on dark energy parameters. We find that for two different kinds of parametrisations of the equation of state parameter $w$, a combination of current data mildly favours an evolving $w$, although the significance is not sufficient for it to be supported by the Bayesian evidence. A forecast of the DESI survey shows that the dynamics of dark energy could be detected at $7σ$ confidence level, and will be decisively supported by the Bayesian evidence, if the best fit model of $w$ derived from current data is the true model.

Probing dynamics of dark energy with latest observations

TL;DR

This study tests the ΛCDM paradigm by using the diagnostic to quantify inter-dataset tension and then reconstructs the dark energy equation of state with two parametrisations of : a polynomial expansion up to and an oscillatory form. It combines diverse cosmological probes (Planck, BAO, Ly, OHD, SNIa, weak lensing) and employs MCMC with Bayesian model selection to assess whether evolving is favored over a cosmological constant. The current data show tension that can be mildly alleviated by dynamical dark energy, notably an oscillatory that crosses , but the Bayesian evidence does not support these extensions over CDM; forecasts with DESI and EuclidSN suggest that future data could detect dynamics at with decisive Bayesian support if the best-fit current model is correct. Overall, the work highlights potential dark energy dynamics as a path to reconcile dataset tensions and underscores the power of forthcoming surveys to decisively test .

Abstract

We examine the validity of the CDM model, and probe for the dynamics of dark energy using latest astronomical observations. Using the diagnosis, we find that different kinds of observational data are in tension within the CDM framework. We then allow for dynamics of dark energy and investigate the constraint on dark energy parameters. We find that for two different kinds of parametrisations of the equation of state parameter , a combination of current data mildly favours an evolving , although the significance is not sufficient for it to be supported by the Bayesian evidence. A forecast of the DESI survey shows that the dynamics of dark energy could be detected at confidence level, and will be decisively supported by the Bayesian evidence, if the best fit model of derived from current data is the true model.

Paper Structure

This paper contains 8 sections, 5 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Method and Data
  3. The $Om$ diagnosis
  4. Parametrisations of the Universe
  5. Observational datasets used
  6. Parameter estimation and model selection
  7. Result
  8. Conclusion and Discussions

Figures (3)

  • Figure 1: The measured $Om$ from various kinds of data: galaxy BAO (blue square), OHD (grey circle) and Lyman-$\alpha$ forest BAO (red triangle). The horizontal cyan, grey and blue bands show the 68% CL allowed values for a constant $Om$ fitted to Planck 2015, OHD and Ly$\alpha$FB respectively. The black solid curve shows $Om$ derived from the best fit $w(z)$ model. See texts for details.
  • Figure 2: Blue bands: the mean with 68% CL error of the reconstructed $w(z)$ using parametrisation I for different orders of the polynomial. The grey band in the $N_{\rm p}=4$ panel shows the nonparametric $w(z)$ reconstruction result in Zhao et al. (2017).
  • Figure 3: Same as Fig. \ref{['fig:poly']} but for parametrisation II. The upper and lower panels show the reconstruction result with and without the $w_4$ parameter fixed respectively.