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Register a new userReconstructing the properties of dark energy from recent observations
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We explore the properties of dark energy from recent observational data, including the Gold Sne Ia, the baryonic acoustic oscillation peak from SDSS, the CMB shift parameter from WMAP3, the X-ray gas mass fraction in cluster and the Hubble parameter versus redshift. The $Lambda CDM$ model with curvature and two parameterized dark energy models are studied. For the $Lambda CDM$ model, we find that the flat universe is consistent with observations at the $1sigma$ confidence level and a closed universe is slightly favored by these data. For two parameterized dark energy models, with the prior given on the present matter density, $Omega_{m0}$, with $Omega_{m0}=0.24$, $Omega_{m0}=0.28$ and $Omega_{m0}=0.32$, our result seems to suggest that the trend of $Omega_{m0}$ dependence for an evolving dark energy from a combination of the observational data sets is model-dependent.
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Studying the effects of dark energy and modified gravity on cosmological scales has led to a great number of physical models being developed. The effective field theory (EFT) of cosmic acceleration allows an efficient exploration of this large model space, usually carried out on a phenomenological basis. However, constraints on such parametrized EFT coefficients cannot be trivially connected to fundamental covariant theories. In this paper we reconstruct the class of covariant Horndeski scalar-tensor theories that reproduce the same background dynamics and linear perturbations as a given EFT action. One can use this reconstruction to interpret constraints on parametrized EFT coefficients in terms of viable covariant Horndeski theories. We demonstrate this method with a number of well-known models and discuss a range of future applications.
We apply a parametric reconstruction method to a homogeneous, isotropic and spatially flat Friedmann-Robertson-Walker (FRW) cosmological model filled of a fluid of dark energy (DE) with constant equation of state parameter interacting with dark matter (DM). The reconstruction method is based on expansions of the general interaction term and the relevant cosmological variables in terms of Chebyshev polynomials which form a complete set orthonormal functions. This interaction term describes an exchange of energy flow between the DE and DM within dark sector. To show how the method works we do the reconstruction of the interaction function expanding it in terms of only the first three Chebyshev polynomials and obtain the best estimation for the coefficients of the expansion as well as for the DE equation of the state constant parameter w using the type Ia Supernova SCP Union data set (307 SNe-Ia). The preliminary reconstruction shows that in the best scenario there is an energy transfer from DM to DE which worsen the problem of the cosmic coincidence in comparison with the LCDM model. We conclude that this fact is an indication of a serious drawback for the existence of such interaction between dark components.
We present a Bayesian technique based on a maximum entropy method to reconstruct the dark energy equation of state $w(z)$ in a non--parametric way. This MaxEnt technique allows to incorporate relevant prior information while adjusting the degree of smoothing of the reconstruction in response to the structure present in the data. After demonstrating the method on synthetic data, we apply it to current cosmological data, separately analysing type Ia supernovae measurement from the HST/GOODS program and the first year Supernovae Legacy Survey (SNLS), complemented by cosmic microwave background and baryonic acoustic oscillations data. We find that the SNLS data are compatible with $w(z) = -1$ at all redshifts $0 leq z lsim 1100$, with errorbars of order 20% for the most constraining choice of priors. The HST/GOODS data exhibit a slight (about $1sigma$ significance) preference for $w>-1$ at $zsim 0.5$ and a drift towards $w>-1$ at larger redshifts, which however is not robust with respect to changes in our prior specifications. We employ both a constant equation of state prior model and a slowly varying $w(z)$ and find that our conclusions are only mildly dependent on this choice at high redshifts. Our method highlights the danger of employing parametric fits for the unknown equation of state, that can potentially miss or underestimate real structure in the data.
We reconstruct the interaction rate between the dark matter and the holographic dark energy with the parameterized equation of states and the future event horizon as the infrared cut-off length. It is shown that the observational constraints from the 192 SNIa and BAO measurement permit the negative interaction in the wide region. Moreover, the usual phenomenological descriptions can not describe the reconstructed interaction well for many cases. The other possible interaction is also discussed.
A large number of cosmological parameters have been suggested for obtaining information on the nature of dark energy. In this work, we study the efficacy of these different parameters in discriminating theoretical models of dark energy, using both currently available supernova (SNe) data, and simulations of future observations. We find that the current data does not put strong constraints on the nature of dark energy, irrespective of the cosmological parameter used. For future data, we find that the although deceleration parameter can accurately reconstruct some dark energy models, it is unable to discriminate between different models of dark energy, therefore limiting its usefulness. Physical parameters such as the equation of state of dark energy, or the dark energy density do a good job of both reconstruction and discrimination if the matter density is known to high accuracy. However, uncertainty in matter density reduces the efficacy of these parameters. A recently proposed parameter, Om(z), constructed from the first derivative of the SNe data, works very well in discriminating different theoretical models of dark energy, and has the added advantage of not being dependent on the value of matter density. Thus we find that a cosmological parameter constructed from the first derivative of the data, for which the theoretical models of dark energy are sufficiently distant from each other, and which is independent of the matter density, performs the best in reconstructing dark energy from SNe data.
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