Latest Data Constraint of Some Parameterized Dark Energy Models
Jing Yang, Xin-Yan Fan, Chao-Jun Feng* , and Xiang-Hua Zhai*
Division of Mathematica and Theoretical Physics, Shanghai Normal University, Shanghai 200234, China
Abstract ：Using various latest cosmological datasets including type-Ia supernovae, cosmic microwave background radiation, baryon acoustic oscillations, and estimations of the Hubble parameter, we test some dark-energy models with parameterized equations of state and try to distinguish or select observation-preferred models. We obtain the best fitting results of the six models and calculate their values of the Akaike information criteria and Bayes information criterion. We can distinguish these dark energy models from each other by using these two information criterions. However, the $\varLambda $CDM model remains the best fit model. Furthermore, we perform geometric diagnostics including statefinder and $Om$ diagnostics to understand the geometric behavior of the dark energy models. We find that the six dark-energy models can be distinguished from each other and from $\varLambda $CDM, Chaplygin gas, quintessence models after the statefinder and $Om$ diagnostics are performed. Finally, we consider the growth factor of the dark-energy models with comparison to the $\varLambda $CDM model. Still, we find the models can be distinguished from each other and from the $\varLambda $CDM model through the growth factor approximation.
收稿日期: 2022-09-30
出版日期: 2022-12-14
:
98.80.-k
(Cosmology)
98.80.Es
(Observational cosmology (including Hubble constant, distance scale, cosmological constant, early Universe, etc))
97.60.Bw
(Supernovae)
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2023, Vol. 40 
