Clustering of B ¯ → D ∗ τ − ν ¯ τ $$ \overline{B}\to {D}^{\left(\ast \right)}{\tau}^{-}{\overline{\nu}}_{\tau } $$ kinematic distributions with ClusterKinG

• Published in: The journal of high energy physics Vol. 2020; no. 4; pp. 1 - 21
• Main Authors: Jason Aebischer, Thomas Kuhr, Kilian Lieret
• Format: Journal Article
• Language: English
• Published: SpringerOpen 01.04.2020
• Subjects: B physics; Beyond Standard Model; e +-e − Experiments; Flavor physics
• ISSN: 1029-8479
• DOI: 10.1007/JHEP04(2020)007

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 chalenging 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 B ¯ → D ∗ τ − ν ¯ τ $$ \overline{B}\to {D}^{\left(\ast \right)}{\tau}^{-}{\overline{\nu}}_{\tau } $$ distributions and discuss various clustering methods and possible implications for future experimental analyses.