Exploring Scotogenic Parameter Spaces and Mapping Uncharted Dark Matter Phenomenology with Multi-Objective Search Algorithms

TL;DR

This paper introduces a multi-objective AI search method to explore complex dark matter models, uncovering diverse phenomenological solutions and advancing the understanding of beyond Standard Model physics.

Contribution

It develops a novel multi-objective optimisation approach for exploring non-minimal scotogenic models, demonstrating improved search capabilities and phenomenological diversity over traditional methods.

Findings

Multi-objective algorithms find more diverse solutions.

Enhanced exploration of parameter space compared to single-objective methods.

Novelty detection identifies rare phenomenological regions.

Abstract

We present a novel artificial intelligence approach to explore beyond Standard Model parameter spaces by leveraging a multi-objective optimisation algorithm. We apply this methodology to a non-minimal scotogenic model which is constrained by Higgs mass, anomalous magnetic moment of the muon, dark matter relic density, dark matter direct detection, neutrino masses and mixing, and lepton flavour violating processes. Our results successfully expand on the phenomenological realisations presented in previous work. We compare between multi- and single-objective algorithms and we observe more phenomenologically diverse solutions and an improved search capacity coming from the former. We use novelty detection to further explore sparsely populated regions of phenomenological interest. These results suggest a powerful search strategy that combines the global exploration of multi-objective optimisation with the exploitation of single-objective optimisation.