Clustering of $\bar B\to D^{(*)}\tau^-\bar\nu_\tau$ kinematic distributions with ClusterKinG

TL;DR

This paper introduces ClusterKinG, a Python package that applies clustering algorithms to kinematic distributions in particle decays, simplifying the analysis of large parameter spaces and aiding future experimental studies.

Contribution

The paper presents a new Python framework, ClusterKinG, for clustering kinematic distributions in high energy physics, facilitating the identification of representative benchmark points.

Findings

ClusterKinG effectively clusters decay distributions.

Clustering reduces complexity in analyzing large parameter spaces.

Potential to improve experimental analysis efficiency.

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 $\bar B\to D^{(*)}\tau^-\bar\nu_\tau$ distributions and discuss various clustering methods and possible implications for future experimental analyses.