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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

Constraining dark energy anisotropic stress

TL;DR: In this paper, a general parametrization of the dark energy component accounting for an equation of state, speed of sound and viscosity was proposed to investigate the possibility of using cosmological observations to probe and constrain an imperfect dark energy fluid.
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Modelling Redshift Space Distortions in Hierarchical Cosmologies

TL;DR: Galaxy redshift surveys allow us to study the 3D spatial distribution of galaxies and clusters and provide accurate radial distances for galaxies in a homogeneous universe as discussed by the authors, however, peculiar velocities are gravitationally induced by inhomogeneous structure and distort the measured distances.
Journal ArticleDOI

Effective field theory of dark energy: A review

TL;DR: The effective field theory of dark energy as mentioned in this paper is based on a Lagrangian description of cosmological perturbations which depends on a number of functions of time, some of which are non-minimal couplings representing genuine deviations from General Relativity.
Journal ArticleDOI

Dark energy from a quintessence (phantom) field rolling near a potential minimum (maximum)

TL;DR: In this paper, the authors examined dark-energy models in which a quintessence or a phantom field rolls near the vicinity of a local minimum or maximum, respectively, of its potential.
Journal ArticleDOI

First-year Sloan Digital Sky Survey-II supernova results: consistency and constraints with other intermediate-redshift data sets

TL;DR: In this paper, the luminosity distances of Type Ia supernovae from the Sloan Digital Sky Survey-II (SDSS-II) SN Survey in conjunction with other intermediate-redshift (z 97 per cent level from this single data set) were analyzed.
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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