Machine Learning insights on the Z3 3HDM with Dark Matter

Abstract

We study a 3-Higgs Doublet Model (3HDM) with an imposed Z3 symmetry, allowing for two Inert scalar doublets and one active Higgs doublet. The WIMP dark matter candidates correspond to two mass-degenerate states, H1 and A1, which possess opposite CP quantum numbers and can reproduce the correct relic density simultaneously with all theoretical and experimental constraints. We use state-of-the-art machine learning algorithms to probe the parameter space of the model by employing an Evolutionary Strategy augmented with Novelty Reward. We consider two situations: a limit for the dark matter mixing angle {\theta} that closes a gauge annihilation channel that would deplete the DM relic density, and the general case without imposing this limit. For both scenarios, we find viable dark matter candidates within two separate mass regimes, ranging from 50 GeV < mDM < mW and 380 < mDM < 1000 GeV. Moreover, we find it is possible to fulfill all the existing constraints while still obtaining values for the dark matter-higgs coupling of order O(0.1). Exploring the model outside the {\theta} = {\pi}/4 limit proved to be an extremely challenging task, as there are regions which seem easy to explore when projected on a 2D plane, yet may be completely disconnected on the hypersurface supporting the valid points, given its non-convex and multi-dimensional nature. We consider new methods of prototype selection to seed new exploration runs, which allow for efficient global scans over the parameter space.