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

中文翻译：

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B \→D ∗τ-ν¯τ$$ \ overline {B} \到{D} ^ {\ left（\ ast \ right）} {\ tau} ^ {-} {\ overline {\ nu}的聚类} _ {\ tau} $$的ClusterKinG运动分布

一个bstract

新物理学可以在粒子衰变的运动学分布中表现出来。定义这种分布形状的参数空间可能很大，这对理论研究和实验研究都是不小的挑战。但是，使用聚类算法，可以将参数空间分解为对应于类似运动学分布的子集（簇）。然后，可以用基准点表示聚类，这可以减少参与程度的研究并简洁地表示结果。我们使用Python程序包ClusterKinG演示了这一概念，这是一个易于使用的用于分布集群的框架，其主要目的是使这些技术在高能物理环境中更易于访问。例如，我们考虑\（\ overline {B} \ to {D} ^ {\ left（\ ast \ right）} {\ tau} ^ {-} {\ overline {\ nu}} _ {\ tau} \）分布并讨论各种聚类方法及其对未来实验分析的可能意义。