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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

How the Scalar Field of Unified Dark Matter Models Can Cluster

TL;DR: In this framework, a technique for reconstructing models where the effective speed of sound is small enough that the scalar field can cluster is proposed, which avoids the strong time evolution of the gravitational potential and the large integrated Sachs–Wolfe effect.
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Anisotropic cosmological solutions in massive vector theories

TL;DR: In this article, the role of a spatial component v of a massive vector field on the anisotropic cosmological background was studied in the context of generalized Proca theories, including the extension to theories higher than second order.
Journal ArticleDOI

Dynamical analysis for a vector-like dark energy

TL;DR: In this article, a dynamical analysis for a vector field as a candidate for the dark energy, in the presence of a barotropic fluid, is presented. But the analysis is restricted to the case where the vector is one component of the so-called cosmic triad, which is a set of three identical copies of an abelian field pointing mutually in orthogonal directions.
Journal ArticleDOI

Constraints on the anisotropy of dark energy

TL;DR: In this article, it was shown that if the equation of state of dark energy is anisotropic, then there will be additional quadrupole anisotropy in the cosmic microwave background induced by the time-dependent aisotropic stress quantified in terms of the mean energy of the observed quadrupoles.
Journal ArticleDOI

Chaotic Fractals at the Root of Relativistic Quantum Physics and Cosmology

TL;DR: In this paper, a short survey of recent achievements of applying nonlinear dynamics to notoriously difficult subjects such as quantum entanglement as well as the origin and true nature of dark energy, negative absolute temperature and the fractal meaning of the constancy of the speed of light is given.
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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