Observational Constraints to Ricci Dark Energy Model by Using: SN, BAO, OHD, fgas Data Sets
Lixin Xu, Yuting Wang

TL;DR
This study constrains the Ricci dark energy model using multiple observational datasets and MCMC analysis, finding that current data favor the standard Lambda-CDM model over Ricci dark energy.
Contribution
It provides the first comprehensive observational constraints on the Ricci dark energy model using combined data and MCMC, including flat and non-flat cases.
Findings
Current data favor Lambda-CDM over Ricci dark energy.
Best-fit parameters are consistent within 1-2 sigma regions.
Non-flat model prefers a slight positive curvature.
Abstract
In this paper, we perform a global constraint on the Ricci dark energy model with both the flat case and the non-flat case, using the Markov Chain Monte Carlo (MCMC) method and the combined observational data from the cluster X-ray gas mass fraction, Supernovae of type Ia (397), baryon acoustic oscillations, current Cosmic Microwave Background, and the observational Hubble function. In the flat model, we obtain the best fit values of the parameters in regions: , , , . In the non-flat model, the best fit parameters are found in regions:, ,…
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