Constraining the dark energy equation of state using Bayes theorem and the Kullback-Leibler divergence

Data-driven model-independent reconstructions of the dark energy equation of state w(z) are presented using Planck 2015 era cosmic microwave background, baryonic acoustic oscillations (BAO), Type Ia supernova (SNIa) and Lyman alpha (Ly alpha) data. These reconstructions identify the w(z) behaviour supported by the data and show a bifurcation of the equation of state posterior in the range 1.5 < z < 3. Although the concordance Lambda cold dark matter (Lambda CDM) model is consistent with the data at all redshifts in one of the bifurcated spaces, in the other, a supernegative equation of state (also known as 'phantom dark energy') is identified within the 1.5 sigma confidence intervals of the posterior distribution. To identify the power of different data sets in constraining the dark energy equation of state, we use a novel formulation of the Kullback-Leibler divergence. This formalism quantifies the information the data add when moving from priors to posteriors for each possible data set combination. The SNIa and BAO data sets are shown to provide much more constraining power in comparison to the Ly alpha data sets. Further, SNIa and BAO constrain most strongly around redshift range 0.1-0.5, whilst the Lya data constrain weakly over a broader range. We do not attribute the supernegative favouring to any particular data set, and note that the Lambda CDM model was favoured at more than 2 log-units in Bayes factors over all the models tested despite the weakly preferred w(z) structure in the data.