Exploring anomalous couplings in Higgs boson pair production through shape analysis

TL;DR

This paper introduces a novel shape analysis method using unsupervised learning to classify Higgs boson pair invariant mass distributions, enhancing the understanding of anomalous couplings in a five-dimensional EFT parameter space.

Contribution

It presents a new shape classification approach with unsupervised learning for analyzing Higgs pair production, improving sensitivity to anomalous couplings.

Findings

Unsupervised learning effectively captures shape features.

Shape analysis reveals impact of anomalous couplings.

Method outperforms traditional shape analysis approaches.

Abstract

We classify shapes of Higgs boson pair invariant mass distributions $m_{hh}$, calculated at NLO with full top quark mass dependence, and visualise how distinct classes of shapes relate to the underlying coupling parameter space. Our study is based on a five-dimensional parameter space relevant for Higgs boson pair production in a non-linear Effective Field Theory framework. We use two approaches: an analysis based on predefined shape types and a classification into shape clusters based on unsupervised learning. We find that our method based on unsupervised learning is able to capture shape features very well and therefore allows a more detailed study of the impact of anomalous couplings on the $m_{hh}$ shape compared to more conventional approaches to a shape analysis.