Topological Landscapes of the BSM Higgs Sector

Abstract

We explore the structure of the parameter space in the Singlet Scalar Dark Matter (SSDM) model and the Next-to-Two Higgs Doublet Model (N2HDM) with $\tan\beta = 5$ and $\tan\beta = 45$. Parameter points are classified as allowed or excluded based on compatibility with the Higgs observation constraints. Using a combined framework of Topological Data Analysis (TDA), Uniform Manifold Approximation and Projection (UMAP), and Linear Discriminant Analysis (LDA), we characterize the global geometry and topology of these high-dimensional landscapes. Our findings reveal that the SSDM and the N2HDM Type~I model exhibit finely tuned islands of collider viability. In the case of N2HDM Type~I, we also find that increasing $\tan\beta$ leads to greater topological fragmentation and higher Betti number persistence, indicating enhanced structural complexity. In contrast, the given choice of parameters excludes the entire N2HDM Type II parameter space based on current Higgs measurements. The topological properties serve as important quantitative descriptors for the phenomenological viability of the BSM frameworks. We leverage the above mentioned topological features to train machine learning models for faster characterization of the BSM Higgs sector into allowed and excluded parameter regions.