Bayesian Analysis and Constraints on Kinematic Models from Union SNIa

Predictions about the kinematic expansion history of the universe are 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 a 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 ΛCDM model. All models characterize an accelerated expansion for the universe today and most models indicate that it was decelerating in the past, but the transition redshift from one phase to the other is model-dependent. We find that even very simple kinematic models are equally good to describe the Supernova data compared to the concordance ΛCDM model, and that the current observations are not powerful enough to discriminate among them. For a constant jerk model the statistical χ analysis yields j0 = −1.9 ± 0.8 (1σ) with the ΛCDM model (j0 = −1) being only marginally compatible at 2σ confidence level.