Higgs Pair Production: Choosing Benchmarks With Cluster Analysis

TL;DR

This paper presents a clustering-based approach to select benchmark points in the parameter space of new physics models, specifically applied to Higgs pair production, to optimize experimental search strategies.

Contribution

It introduces a practical method using cluster analysis to categorize parameter space regions by kinematic features for Higgs pair production studies.

Findings

Cluster analysis effectively groups parameter space regions.

Benchmark points represent large areas of parameter space.

Enhanced Higgs pair production signals are identified.

Abstract

New physics theories often depend on a large number of free parameters. The precise values of those parameters in some cases drastically affect the resulting phenomenology of fundamental physics processes, while in others finite variations can leave it basically invariant at the level of detail experimentally accessible. 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 of different models; a clustering algorithm using that metric may then allow the division of the space into homogeneous regions, each of which can be successfully represented by a benchmark point. Searches targeting those benchmark points are then guaranteed to be sensitive to a large area of the parameter space. In this document we show a practical implementation of the above strategy for the study of non-resonant production of Higgs boson pairs in the context of extensions of the standard model with anomalous couplings of the Higgs bosons. A non-standard value of those couplings may significantly enhance the Higgs pair production cross section, such that the process could be detectable with the data that the Large Hadron Collider will collect in Run 2.