Bayesian model-independent evaluation of expansion rates of the universe

TL;DR

This paper uses Bayesian, model-independent methods to evaluate the universe's expansion rates with supernova data, finding results that challenge prior assumptions and highlight the importance of prior choices.

Contribution

It introduces a Bayesian, model-independent approach using Taylor series expansion of the scale factor, avoiding convergence issues of redshift-based methods.

Findings

Marginal likelihood for deceleration parameter peaked around zero.

New data significantly constrains cosmographic expansion rates.

Results depend on the prior distribution of the Hubble parameter.

Abstract

Marginal likelihoods for the cosmic expansion rates are evaluated using the `Constitution' data of 397 supernovas, thereby updating the results in some previous works. Even when beginning with a very strong prior probability that favors an accelerated expansion, we obtain a marginal likelihood for the deceleration parameter $q_0$ peaked around zero in the spatially flat case. It is also found that the new data significantly constrains the cosmographic expansion rates, when compared to the previous analyses. These results may strongly depend on the Gaussian prior probability distribution chosen for the Hubble parameter represented by $h$, with $h=0.68\pm 0.06$. This and similar priors for other expansion rates were deduced from previous data. Here again we perform the Bayesian model-independent analysis in which the scale factor is expanded into a Taylor series in time about the present epoch. Unlike such Taylor expansions in terms of redshift, this approach has no convergence problem.