  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.
