scispace - formally typeset
Journal ArticleDOI

Dynamics of dark energy

Reads0
Chats0
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.

read more

Citations
More filters
Journal ArticleDOI

Observational constraints on oscillating dark-energy parametrizations

TL;DR: In this article, a detailed confrontation of various oscillating dark-energy parametrizations with the latest sets of observational data is performed, and the best-fit characters of almost all models are bent towards the phantom region; nevertheless, in all of them, the quintessential regime is also allowed within $1\ensuremath{\sigma}$ confidence level.
Journal ArticleDOI

Reconstruction procedure in nonlocal cosmological models

TL;DR: In this article, a nonlocal gravity model is considered which does not assume the existence of a new dimensional parameter in the action and includes a function f(−1R), with □ the d'Alembertian operator.
Journal ArticleDOI

The cosmic snap parameter in f(R) gravity

TL;DR: In this paper, the authors derived the expression for the snap parameter in f(R) gravity and used the Palatini variational principle to obtain the field equations and regard the Einstein conformal frame as physical.
Journal ArticleDOI

Introduction to the application of dynamical systems theory in the study of the dynamics of cosmological models of dark energy

TL;DR: In this paper, a mostly pedagogical introduction to the cosmological application of the basic tools of dynamical systems theory is presented, showing that, in spite of their amazing simplicity, these tools allow us to extract essential information on the asymptotic dynamics of a wide variety of cosmology models.
Journal ArticleDOI

Holographic dark energy in Brans-Dicke theory with logarithmic correction

TL;DR: In this article, the cosmological implication of the interacting entropy-corrected holographic dark energy model in the framework of Brans-Dicke cosmology is studied.
References
More filters
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.
Related Papers (5)