Clustering of $\bar{B}\to D^{(\ast )}{\tau }^{-}{\bar{\nu }}_{\tau }$ kinematic distributions with ClusterKinG

Abstract

New Physics can manifest itself in kinematic distributions of particle decays. The parameter space defining the shape of such distributions can be large which is challenging for both theoretical and experimental studies. Using clustering algorithms, the parameter space can however be dissected into subsets (clusters) which correspond to similar kinematic distributions. Clusters can then be represented by benchmark points, which allow for less involved studies and a concise presentation of the results. We demonstrate this concept using the Python package ClusterKinG, an easy to use framework for the clustering of distributions that particularly aims to make these techniques more accessible in a High Energy Physics context. As an example we consider $\bar{B}\to D^{(\ast )}{\tau }^{-}{\bar{\nu }}_{\tau }$ distributions and discuss various clustering methods and possible implications for future experimental analyses.

Comments: 25 pages, 10 figures, 2 tables; corrected implementation of chi2 metric with correlated random variables and corrected number of degrees of freedom; added toy study to validate statistical treatment