Crossing the phantom divide: theoretical implications and observational status

Nesseris, Savvas; Perivolaropoulos, Leandros

doi:10.1088/1475-7516/2007/01/018

Cited by 339 publications

(328 citation statements)

References 128 publications

(224 reference statements)

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“…[26][27][28]. Therefore, by fitting the SnIa data with the Genetic Algorithm we directly reconstruct the luminosity distance D L , which from now on we will call…”

Section: The Data a Supernovae Of Type Iamentioning

confidence: 99%

A new perspective on dark energy modeling via genetic algorithms

Nesseris¹,

García-Bellido²

2012

J. Cosmol. Astropart. Phys.

118

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We use Genetic Algorithms to extract information from several cosmological probes, such as the type Ia supernovae (SnIa), the Baryon Acoustic Oscillations (BAO) and the growth rate of matter perturbations. This is done by implementing a model independent and bias-free reconstruction of the various scales and distances that characterize the data, like the luminosity dL(z) and the angular diameter distance dA(z) in the SnIa and BAO data, respectively, or the dependence with redshift of the matter density Ωm(a) in the growth rate data, f σ8(z). These quantities can then be used to reconstruct the expansion history of the Universe, and the resulting Dark Energy (DE) equation of state w(z) in the context of FRW models, or the mass radial function ΩM (r) in LTB models. In this way, the reconstruction is completely independent of our prior bias. Furthermore, we use this method to test the Etherington relation, ie the well-known relation between the luminosity and the, which is equal to 1 in metric theories of gravity. We find that the present data seem to suggest a 3-σ deviation from one at redshifts z ∼ 0.5. Finally, we present a novel way, within the Genetic Algorithm paradigm, to analytically estimate the errors on the reconstructed quantities by calculating a Path Integral over all possible functions that may contribute to the likelihood. We show that this can be done regardless of the data being correlated or uncorrelated with each other and we also explicitly demonstrate that our approach is in good agreement with other error estimation techniques like the Fisher Matrix approach and the Bootstrap Monte Carlo.

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“…[26][27][28]. Therefore, by fitting the SnIa data with the Genetic Algorithm we directly reconstruct the luminosity distance D L , which from now on we will call…”

Section: The Data a Supernovae Of Type Iamentioning

confidence: 99%

A new perspective on dark energy modeling via genetic algorithms

Nesseris¹,

García-Bellido²

2012

J. Cosmol. Astropart. Phys.

118

View full text Add to dashboard Cite

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“…On the other hand, a very accurate and deep geometrical probe of dark energy is the angular scale of the sound horizon at the last scattering surface as encoded in the location l T T 1 of the first peak of the Cosmic Microwave Background (CMB) temperature perturbation spectrum. This probe is described by the CMB shift parameter [45,46,47] which is defined as…”

Section: Likelihood Analysismentioning

confidence: 99%

Spherical collapse model and cluster formation beyond theΛcosmology: Indications for a clustered dark energy?

2009

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We generalize the small scale dynamics of the universe by taking into account models with an equation of state which evolves with time, and provide a complete formulation of the cluster virialization attempting to address the nonlinear regime of structure formation. In the context of the current dark energy models, we find that galaxy clusters appear to form at z ∼ 1 − 2, in agreement with previous studies. Also, we investigate thoroughly the evolution of spherical matter perturbations, as the latter decouple from the background expansion and start to "turn around" and finally collapse. Within this framework, we find that the concentration parameter depends on the choice of the considered dark energy (homogeneous or clustered). In particular, if the distribution of the dark energy is clustered then we produce more concentrated structures with respect to the homogeneous dark energy. Finally, comparing the predicted concentration parameter with the observed concentration parameter, measured for four massive galaxy clusters, we find that the scenario which contains a pure homogeneous dark energy is unable to reproduce the data. The situation becomes somewhat better in the case of an inhomogeneous (clustered) dark energy.PACS numbers: 98.80.-k, 95.35.+d, 95.36.+x

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“…Also 1 + β is a factor taking into account the fact that the total baryonic mass in clusters consists of both X-ray gas and optically luminous baryonic mass (stars), the later being proportional to the former with proportionality constant β ≃ 0.19 √ h [54]. Dimensionless parameter for this observation is given by [74]:…”

Section: Observational Constraints From the Background Evolutionmentioning

confidence: 99%

Observational Constraints with Recent Data on the DGP Modified Gravity

2008

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We study one of the simplest covariant modified-gravity models based on the Dvali-Gabadadze-Porrati (DGP) brane cosmology, a self-accelerating universe. In this model gravitational leakage into extra dimensions is responsible of late-time acceleration. We mainly focus on the effects of the model parameters on the geometry and the age of universe. Also we investigate the evolution of matter density perturbations in the modified gravity model, and obtain an analytical expression for the growth index, f . We show that increasing Ωr c leads to less growth of the density contrast δ, and also decreases the growth index. We give a fitting formula for the growth index at the present time and indicate that dominant term in this expression verifies the well-known approximation relation f ≃ Ω γ m . As the observational test, the new Supernova Type Ia (SNIa) Gold sample and Supernova Legacy Survey (SNLS) data, size of baryonic acoustic peak from Sloan Digital Sky Survey (SDSS), the position of the acoustic peak from the CMB observations and the Cluster Baryon Gas Mass Fraction (gas) are used to constrain the parameters of the DGP model. We also combine previous results with large scale structure formation (LSS) from the 2dFGRS survey. Finally to check the consistency of the DGP model, we compare the age of old cosmological objects with age of universe in this model.

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Crossing the phantom divide: theoretical implications and observational status

Cited by 339 publications

References 128 publications

A new perspective on dark energy modeling via genetic algorithms

A new perspective on dark energy modeling via genetic algorithms

Spherical collapse model and cluster formation beyond theΛcosmology: Indications for a clustered dark energy?

Observational Constraints with Recent Data on the DGP Modified Gravity

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