scholarly journals Observational constraints on the interacting Ricci dark energy model

2010 ◽  
Vol 81 (2) ◽  
Author(s):  
Masashi Suwa ◽  
Takeshi Nihei
2013 ◽  
Vol 28 (39) ◽  
pp. 1350171 ◽  
Author(s):  
PENG HUANG ◽  
YONG-CHANG HUANG ◽  
FANG-FANG YUAN

Commonly used boundary conditions in reconstructing f(T) gravity from holographic Ricci dark energy (RDE) model are found to cause some problem, we therefore propose new boundary conditions in this paper. By reconstructing f(T) gravity from the RDE with these new boundary conditions, we show that the new ones are better than the present commonly used ones since they can give the physically expected information, which is lost when the commonly used ones are taken in the reconstruction, of the resulting f(T) theory. Thus, the new boundary conditions proposed here are more suitable for the reconstruction of f(T) gravity.


Author(s):  
H. Hossienkhani ◽  
N. Azimi ◽  
H. Yousefi

The impact of anisotropy on the Ricci dark energy cosmologies is investigated where it is assumed that the geometry of the universe is described by Bianchi type I (BI) metric. The main goal is to determine the astrophysical constraints on the model by using the current available data as type Ia supernovae (SNIa), the Baryon Acoustic Oscillation (BAO), and the Hubble parameter [Formula: see text] data. In this regard, a maximum likelihood method is applied to constrain the cosmological parameters. Combining the data, it is found out that the allowed range for the density parameter of the model stands in [Formula: see text]. With the help of the Supernova Legacy Survey (SNLS) sample, we estimate the possible dipole anisotropy of the Ricci dark energy model. Then, by using a standard [Formula: see text] minimization method, it is realized that the transition epoch from early decelerated to current accelerated expansion occurs faster in Ricci dark energy model than [Formula: see text]CDM model. The results indicate that the BI model for the Ricci dark energy is consistent with the observational data.


Author(s):  
Mariam Bouhmadi-López ◽  
Ahmed Errahmani ◽  
Taoufik Ouali ◽  
Yaser Tavakoli

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