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

An Erratum to this article was published on 18 May 2021

This article has been updated

A preprint version of the article is available at arXiv.


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 \( \overline{B}\to {D}^{\left(\ast \right)}{\tau}^{-}{\overline{\nu}}_{\tau } \) distributions and discuss various clustering methods and possible implications for future experimental analyses.

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Correspondence to Kilian Lieret.

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Aebischer, J., Kuhr, T. & Lieret, K. Clustering of \( \overline{B}\to {D}^{\left(\ast \right)}{\tau}^{-}{\overline{\nu}}_{\tau } \) kinematic distributions with ClusterKinG. J. High Energ. Phys. 2020, 7 (2020).

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  • B physics
  • Beyond Standard Model
  • e+-e Experiments
  • Flavor physics