Information Gains in Cosmological Parameter Estimation

Combining datasets from different experiments and probes to constrain cosmological models is an important challenge in observational cosmology. We summarize a framework for measuring the constraining power and the consistency of separately or jointly analyzed data within a given model that we proposed in earlier work (Seehars et al. 2014). Applying the Kullback-Leibler divergence to posterior distributions, we can quantify the difference between constraints and distinguish contributions from gains in precision and shifts in parameter space. We show results from applying this technique to a combination of datasets and probes such as the cosmic microwave background or baryon acoustic oscillations.