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Bayesian Analysis and Constraints on Kinematic Models from Union SNIa

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arxiv 0904.3550 v3 pith:AG54PN5Y submitted 2009-04-22 astro-ph.CO

classification astro-ph.CO
keywords modelscosmickinematicdecelerationjerklambdamodeltransition
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abstract

The kinematic expansion history of the universe is investigated by using the 307 supernovae type Ia from the Union Compilation set. Three simple model parameterizations for the deceleration parameter (constant, linear and abrupt transition) and two different models that are explicitly parametrized by the cosmic jerk parameter (constant and variable) are considered. Likelihood and Bayesian analyses are employed to find best fit parameters and compare models among themselves and with the flat $\Lambda$CDM model. Analytical expressions and estimates for the deceleration and cosmic jerk parameters today ($q_0$ and $j_0$) and for the transition redshift ($z_t$) between a past phase of cosmic deceleration to a current phase of acceleration are given. All models characterize an accelerated expansion for the universe today and largely indicate that it was decelerating in the past, having a transition redshift around 0.5. The cosmic jerk is not strongly constrained by the present supernovae data. For the most realistic kinematic models the $1\sigma$ confidence limits imply the following ranges of values: $q_0\in[-0.96,-0.46]$, $j_0\in[-3.2,-0.3]$ and $z_t\in[0.36,0.84]$, which are compatible with the $\Lambda$CDM predictions, $q_0=-0.57\pm0.04$, $j_0=-1$ and $z_t=0.71\pm0.08$. We find that even very simple kinematic models are equally good to describe the data compared to the concordance $\Lambda$CDM model, and that the current observations are not powerful enough to discriminate among all of them.

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  1. Gaussian Process Estimation of Transition Redshift

    astro-ph.CO 2019-08 conditional novelty 4.0 of 10

    From Hubble parameter and Type Ia supernova data, Gaussian Process reconstruction places the cosmic deceleration-to-acceleration transition at z_t ≈ 0.59 and z_t ≈ 0.68, respectively.

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