2017
DOI: 10.1007/s11433-017-9111-4
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Diagnosing holographic type dark energy models with the Statefinder hierarchy, composite null diagnostic and w-w′ pair

Abstract: The main purpose of this work is to distinguish various holographic type dark energy (DE) models, including the ΛHDE, HDE, NADE and RDE model, by using various diagnostic tools. The first diagnostic tool is the Statefinder hierarchy, in which the evolution of Statefinder hierarchy parmeter S 4 can lead to larger differences than S (1) 3 , while the CND pair has a stronger ability to distinguish different models than the Statefinder hierarchy. (3) For the case of varying C, the {w, w } pair has qualitative impa… Show more

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Cited by 20 publications
(3 citation statements)
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“…Statefinder hierarchy [72][73][74] is an effective diagnosis of geometry. It is appropriated to discriminate, various DE models from the ΛCDM model by utilizes the higher order derivative of the scale factor.…”
Section: Statefinder Hierarchymentioning
confidence: 99%
See 1 more Smart Citation
“…Statefinder hierarchy [72][73][74] is an effective diagnosis of geometry. It is appropriated to discriminate, various DE models from the ΛCDM model by utilizes the higher order derivative of the scale factor.…”
Section: Statefinder Hierarchymentioning
confidence: 99%
“…This equation can be numerically solved for the condition f (z = 0) = 1 for the ΛCDM and the BHDE model. Depending on f (z), other null diagnostic, known as fractional growth parameter ǫ, is discussed in [56,73].…”
Section: Growth Rate Of Perturbationsmentioning
confidence: 99%
“…In this paper, we will focus on the background evolution of interacting dark energy model. In addition, we also have explored the deviation of the interaction dark energy model from ΛCDM and the behavior of evolution trajectory when the interaction dark energy model takes different parameter values through the State f inder hierarchy [56,[66][67][68][69][70][71][72][73][74] and Om [75][76][77] diagnostic. The state f inder hierarchy is a sensitive diagnostic of DE, which uses the higher-order derivatives of a(t) and can break the degeneracy of the model very well.…”
Section: Introductionmentioning
confidence: 99%