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Dynamics of dark energy

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TLDR
In this article, the authors review the observational evidence for the current accelerated expansion of the universe and present a number of dark energy models in addition to the conventional cosmological constant, paying particular attention to scalar field models such as quintessence, K-essence and tachyon.
Abstract
We review in detail a number of approaches that have been adopted to try and explain the remarkable observation of our accelerating universe. In particular we discuss the arguments for and recent progress made towards understanding the nature of dark energy. We review the observational evidence for the current accelerated expansion of the universe and present a number of dark energy models in addition to the conventional cosmological constant, paying particular attention to scalar field models such as quintessence, K-essence, tachyon, phantom and dilatonic models. The importance of cosmological scaling solutions is emphasized when studying the dynamical system of scalar fields including coupled dark energy. We study the evolution of cosmological perturbations allowing us to confront them with the observation of the Cosmic Microwave Background and Large Scale Structure and demonstrate how it is possible in principle to reconstruct the equation of state of dark energy by also using Supernovae Ia observational data. We also discuss in detail the nature of tracking solutions in cosmology, particle physics and braneworld models of dark energy, the nature of possible future singularities, the effect of higher order curvature terms to avoid a Big Rip singularity, and approaches to modifying gravity which leads to a late-time accelerated expansion without recourse to a new form of dark energy.

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Citations
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Journal ArticleDOI

Effective action approach to cosmological perturbations in dark energy and modified gravity

TL;DR: In this paper, the components of the perturbed dark energy momentum tensor which appears in the perturb generalized gravitational field equations are constructed in terms of background dependent functions, which can be used to specify the model completely.
Journal ArticleDOI

Dynamics of the anisotropic Kantowsky-Sachs geometries in $R^n$ gravity

TL;DR: In this paper, a detailed phase-space analysis of the universe is performed, and it is shown that at late times the universe can result in a state of accelerating expansion, and additionally, for a particular n-range (2 < n < 3), it exhibits phantom behavior.
Journal ArticleDOI

Bianchi Type III Anisotropic Dark Energy Models with Constant Deceleration Parameter

TL;DR: In this article, the Bianchi type III dark energy model with constant deceleration parameter is investigated and the equation of state parameter ω is found to be time dependent and its existing range for this model is consistent with the recent observations of SN Ia data, with CMBR anisotropy and galaxy clustering statistics.
Journal ArticleDOI

Dark spinor models in gravitation and cosmology

TL;DR: In this article, the authors define an entire class of field theories based on non-standard spinors, referred to as Dark Spinors, and provide a critical analysis of previous proposals for dark spinors noting that they violate Lorentz invariance.
Journal ArticleDOI

Holographic dark-energy models

TL;DR: In this paper, different holographic dark energy models are studied from a unified point of view, and the relation between the equation-of-state parameter and the energy density ratio of both components for each of the choices, as well as the possibility of noninteracting and scaling solutions are discussed.
References
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Journal ArticleDOI

A new look at the statistical model identification

TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
Journal ArticleDOI

Estimating the Dimension of a Model

TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.

Estimating the dimension of a model

TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
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