Pandemonium: a clustering tool to partition parameter space -- application to the B anomalies

Abstract: We introduce the interactive tool pandemonium to cluster model predictions that depend on a set of parameters. The model predictions are used to define the coordinates in observable space which go into the clustering. The results of this partitioning are then visualized in both observable and parameter space to study correlations between them. The tool offers multiple choices for coordinates, distance functions and linkage methods within hierarchical clustering. It provides a set of diagnostic statistics and visualization methods to study the clustering results in order to interpret the outcome. The methods are most useful in an interactive environment that enables exploration, and we have implemented them with a graphical user interface in R. We demonstrate the concepts with an application to phenomenological studies in flavor physics in the context of the so-called B anomalies.