Clustering and visualization tools to study high dimensional parameter spaces: B anomalies example

Abstract

We describe the applications of clustering and visualization tools using the so-called neutral B anomalies as an example. Clustering permits parameter space partitioning into regions that can be separated with some given measurements. It provides a visualization of the collective dependence of all the observables on the parameters of the problem. These methods highlight the relative importance of different observables, and the effect of correlations, and help to understand tensions in global fits. The tools we describe also permit a visual inspection of high dimensional observable and parameter spaces through both linear projections and slicing.