Exploring anomalous couplings in Higgs boson pair production through shape analysis

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.