Figure of merit and different combinations of observational data sets

Su, Qi-Ping; Tuo, Zhong-Liang; Cai, Rong-Gen

doi:10.1103/physrevd.84.103519

Cited by 9 publications

(5 citation statements)

References 92 publications

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“…Here we adopt the estimation by Union2 data in ref. [67], namely, matter density Ω m = 0.419 +0.090 −0.028 , parameters w 0 = −0.86 +0.46 −0.32 and w a = −5.51 +6.99 −8.79 . In figure 10, we plot the reconstructions in CPL model and GP method.…”

Section: Tension Analysis For Union2 Datamentioning

confidence: 99%

Test of the cosmic evolution using Gaussian processes

Zhang

Xia

2016

J. Cosmol. Astropart. Phys.

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Much focus was on the possible slowing down of cosmic acceleration under the dark energy parametrization. In the present paper, we investigate this subject using the Gaussian processes (GP), without resorting to a particular template of dark energy. The reconstruction is carried out by abundant data including luminosity distance from Union2, Union2.1 compilation and gamma-ray burst, and dynamical Hubble parameter. It suggests that slowing down of cosmic acceleration cannot be presented within 95% C.L., in considering the influence of spatial curvature and Hubble constant. In order to reveal the reason of tension between our reconstruction and previous parametrization constraint for Union2 data, we compare them and find that slowing down of acceleration in some parametrization is only a "mirage". Although these parameterizations fits well with the observational data, their tension can be revealed by high order derivative of distance D. Instead, GP method is able to faithfully model the cosmic expansion history.

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Section: Tension Analysis For Union2 Datamentioning

confidence: 99%

Test of the cosmic evolution using Gaussian processes

Zhang

Xia

2016

J. Cosmol. Astropart. Phys.

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“…To derive a better constraint on cosmological parameters, one often makes a joint analysis with different combinations of observational data sets. Recently, Su et al (2011) found that the combination of SNeIa, shift parameter R based on CMB data, distance parameter A based on BAO data, and observational H(z) data (OHD) is a better choice using the criterion which estimates the constraining power of cosmological data. In this paper, we adopt this scheme.…”

Section: Observational Datamentioning

confidence: 99%

“…However, an alternative way can be carried out following previous work (Nesseris & Perivolaropoulos 2005;Perivolaropoulos 2005;di Pietro & Claeskens 2003). The χ 2 of Equation ( 13) with respect to µ 0 can eventually be transformed into a modified expression (Su et al 2011)…”

Section: Luminosity Distancementioning

confidence: 99%

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Redshift drift reconstruction for some cosmological models from observations

Zhang

Liu

2013

Res. Astron. Astrophys.

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Redshift drift is a tool to directly probe the expansion history of the universe. Based on the Friedmann-Robertson-Walker framework, we reconstruct the velocity drift and deceleration factor for several cosmological models using observational H(z) data from the differential ages of galaxies and baryon acoustic oscillation peaks, luminosity distance of Type Ia supernovae, cosmic microwave background shift parameter, and baryon acoustic oscillation distance parameter. They can, for the first time, provide an objective and quantifiable measure of the redshift drift. We find that reconstructed velocity drift with different peak values and corresponding redshifts can potentially provide a method to distinguish the quality of competing dark energy models at low redshifts. Better fitting between models and observational data indicate that current data are insufficient to distinguish the quality of these models. However, by comparing with the simulated velocity drift from Liske et al, we find that the Dvali-Gabadadze-Porrati model is inconsistent with the data at high redshift, which originally piqued the interest of researchers in the topic of redshift drift. Considering the deceleration factor, we are able to give a stable instantaneous estimation of a transition redshift of z t ∼ 0.7 from joint constraints, which incorporates a more complete set of values than the previous study that used a single data set.

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“…One way of studying the nature of dark energy is through the observational data. There are many model-independent studies on the nature of dark energy by using the observational data [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39]. In particular, one usually parameterizes the energy density Ω φ (z) or the equation of state parameter w(z) of dark energy.…”

Section: Introductionmentioning

confidence: 99%

On the effect of the degeneracy among dark energy parameters

Gong

Gao

2014

Eur. Phys. J. C

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The dynamics of scalar fields as dark energy is well approximated by some general relations between the equation of state parameter w(z) and the fractional energy density Ω φ . Based on the approximation, for slowly rolling scalar fields, we derived the analytical expressions of w(z) which reduce to the popular Chevallier-PolarskiLinder parametrization with an explicit degeneracy relation between w 0 and w a . The models approximate the dynamics of scalar fields well and help eliminate the degeneracies among w a , w 0 , and Ω m0 . With the explicit degeneracy relations, we test their effects on the constraints of the cosmological parameters. We find that: (1) The analytical relations between w 0 and w a for the two models are consistent with observational data. (2) The degeneracies have little effect on Ω m0 . (3) The 1σ error of w 0 was reduced about 30 % with the degeneracy relations.

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Figure of merit and different combinations of observational data sets

Cited by 9 publications

References 92 publications

Test of the cosmic evolution using Gaussian processes

Test of the cosmic evolution using Gaussian processes

Redshift drift reconstruction for some cosmological models from observations

On the effect of the degeneracy among dark energy parameters

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