Using<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>H</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>z</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math>data as a probe of the concordance model

Seikel, Marina; Yahya, Sahba; Maartens, Roy; Clarkson, Chris

doi:10.1103/physrevd.86.083001

Cited by 116 publications

(120 citation statements)

References 30 publications

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“…according to the analysis made in [29], where they consider various assumed models and many realizations of mock data sets for a test and conclude that the Matérn (ν = 9/2) covariance function can lead to more reliable results than all others when applying GP to reconstructions using D measurements. Following Refs.…”

Section: A Gaussian Processesmentioning

confidence: 99%

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Reconstructing the interaction between dark energy and dark matter using Gaussian processes

2015

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We present a nonparametric approach to reconstruct the interaction between dark energy and dark matter directly from SNIa Union 2.1 data using Gaussian processes, which is a fully Bayesian approach for smoothing data. In this method, once the equation of state (w) of dark energy is specified, the interaction can be reconstructed as a function of redshift. For the decaying vacuum energy case with w = −1, the reconstructed interaction is consistent with the standard ΛCDM model, namely, there is no evidence for the interaction. This also holds for the constant w cases from −0.9 to −1.1 and for the Chevallier-Polarski-Linder (CPL) parametrization case. If the equation of state deviates obviously from −1, the reconstructed interaction exists at 95% confidence level. This shows the degeneracy between the interaction and the equation of state of dark energy when they get constraints from the observational data.PACS numbers: 95.36.+x, 98.80.Es

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Section: A Gaussian Processesmentioning

confidence: 99%

“…We use Gaussian processes in Python (GaPP) [27] to derive our GP results. This GP code has been applied in many papers [27][28][29][30][31][32][33]. The distribution over functions provided by GP is suitable to describe the observed data.…”

Section: A Gaussian Processesmentioning

confidence: 99%

Reconstructing the interaction between dark energy and dark matter using Gaussian processes

2015

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“…In recent years H(z) measurements of the Hubble parameter have been used to constrain several cosmological parameters [58,59,60,61,62,63]. In this paper, we use 19 H(z) measurements from cosmic chonometers [58,59,62] plus 7 H(z) measurements from baryon acoustic oscillations (BAOs) [64,65,66,67,68] in the redshift range 0 < z < 2.3.…”

Section: B H(z) Measurementsmentioning

confidence: 99%

Probing cosmic opacity at high redshifts with gamma-ray bursts

Holanda

Busti

2014

Phys. Rev. D

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Probing the evolution of the universe at high redshifts with standard candles is a powerful way to discriminate dark energy models, where an open question nowadays is whether this component is constant or evolves with time. One possible source of ambiguity in this kind of analyses comes from cosmic opacity, which can mimick a dark enery behaviour. However, most tests of cosmic opacity have been restricted to the redshift range z < 2. In this work, by using luminosity distances of gamma-ray bursts (GRBs), given the validity of the Amati relation, and the latest H(z) data we determine constraints on the cosmic opacity at high redshifts (z > 2) for a flat ΛCDM model. A possible degenerescence of the results with the adopted cosmological model is also investigated by considering a flat XCDM model. The limits on cosmic opacity in the redshift range 0 < z < 2 are updated with type Ia supernovae (SNe Ia) from the Union2.1 sample, where we added the most distant (z = 1.713) spectroscopically confirmed SNe Ia. From the analyses performed, we find that both SNe Ia and GRBs samples are compatible with a transparent universe at 1σ level and the results are independent of the dark energy equation of state parameter w.

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“…Instead, it characterizes the typical changes of the function. The detailed analysis and description of the GP method can be found in [16,19].…”

Section: A Gaussian Processesmentioning

confidence: 99%

“…Here, we use the Gaussian processes in Python (GaPP) [16]. This GP code has been used in various papers for different studies [16][17][18][19][20][21][22][23][24]. The distribution over functions provided by GP is suitable to describe the observational data.…”

Section: A Gaussian Processesmentioning

confidence: 99%

Null test of the cosmic curvature usingH(z)and supernovae data

2016

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We introduce a model-independent approach to the null test of the cosmic curvature which is geometrically related to the Hubble parameter H(z) and luminosity distance dL(z). Combining the independent observations of H(z) and dL(z), we use the model-independent smoothing technique, Gaussian processes, to reconstruct them and determine the cosmic curvature Ω (0) K in the null test relation. The null test is totally geometrical and does not assume any cosmological model. We show that the cosmic curvature Ω (0) K = 0 is consistent with current observational data sets, falling within the 1σ limit. To demonstrate the effect on the precision of the null test, we produce a series of simulated data of the models with different Ω 98.80.Es, 98.80Jk

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UsingH(z)data as a probe of the concordance model

Cited by 116 publications

References 30 publications

Reconstructing the interaction between dark energy and dark matter using Gaussian processes

Reconstructing the interaction between dark energy and dark matter using Gaussian processes

Probing cosmic opacity at high redshifts with gamma-ray bursts

Null test of the cosmic curvature usingH(z)and supernovae data

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