Bayesian distances for quantifying tensions in cosmological inference and the surprise statistic

Tensions between cosmological parameters derived through different channels can be a genuine signature of new physics that $\Lambda$CDM as the standard model is not able to reproduce, in particular in the missing consistency between parameter estimates from measurements the early and late Universe. Or, they could be caused by yet to be understood systematics in the measurements as a more mundane explanation. Commonly, cosmological tensions are stated in terms of mismatches of the posterior parameter distributions, often assuming Gaussian statistics. More importantly, though, would be a quantification if two data sets are consistent to each other before combining them into a joint measurement, ideally isolating hints at individual data points that have a strong influence in generating the tension. For this purpose, we start with statistical divergences applied to posterior distributions following from different data sets and develop the theory of a Fisher metric between two data sets, in analogy to the Fisher metric for different parameter choices. As a topical example, we consider the tension in the Hubble-Lema\^itre constant $H_0$ from supernova and measurements of the cosmic microwave background, derive a ranking of data points in order of their influence on the tension on $H_0$. For this particular example, we compute Bayesian distance measures and show that in the light of CMB data, supernovae are commonly too bright, whereas the low-$\ell$ CMB spectrum is too high, in agreement with intuition about the parameter sensitivity.