Machine Learning insights on the Z3 3HDM with Dark Matter

TL;DR

This paper uses advanced machine learning techniques to explore a Z3 symmetric 3HDM with dark matter candidates, identifying viable parameter regions consistent with all constraints and demonstrating the model's rich phenomenology.

Contribution

It introduces a novel application of evolutionary algorithms with novelty rewards for efficient global parameter space exploration in a complex dark matter model.

Findings

Viable dark matter mass ranges identified: 50 GeV to mW and 380 to 1000 GeV.

Dark matter-higgs coupling can be of order 0.1 while satisfying constraints.

Exploration outside the { heta} = {rac{C}4} limit is highly challenging due to the complex parameter space.

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.