Clustering of $B\to D\tau\nu$ kinematic distributions with the ClusterKinG package

by Jason Aebischer (TUM)

Monday 24 Jun 2019, 11:15 → 12:15 Europe/Zurich

BSP 626 (EPFL)

New physics theories often depend on a large number of free parameters. The phenomenology they predict for physics processes is in some cases drastically affected by the precise value of those free parameters, while in other cases is left basically invariant at the level of the experimental precision. When designing a strategy for the analysis of experimental data in the search for a signal predicted by a new physics model, it appears advantageous to categorize the parameter space describing the model according to the corresponding kinematical features of the final state. A multi-dimensional test statistic can be used to gauge the degree of similarity in the kinematics predicted by different models; a clustering algorithm using that metric may allow the division of the parameter space into homogeneous regions, each of which can be successfully represented by a benchmark point. Searches targeting those benchmarks are then guaranteed to be sensitive to a large area of the parameter space. I will present a tool called ClusterKinG which allows to perform such a clustering of the parameter space in an automated way. As a concrete example I will discuss kinematic distributions of the process $B\to D\tau\nu$

ClusterKinG package

EPFL_aebischer.pdf