Minimal Consistent Dark Matter models for systematic experimental characterisation: Fermion Dark Matter
Alexander Belyaev, Giacomo Cacciapaglia, Daniel Locke, Alexander, Pukhov

TL;DR
This paper introduces a comprehensive classification of minimal, consistent fermionic Dark Matter models that adhere to Standard Model symmetries, aiding systematic experimental searches.
Contribution
It provides the first complete, model-independent framework linking experimental bounds with top-down Dark Matter theories, considering renormalisability and symmetry invariance.
Findings
Identifies unexplored viable Dark Matter models.
Reevaluates one-loop direct detection contributions.
Highlights the importance of mass splits in Dark multiplets.
Abstract
The search for a Dark Matter particle is the new grail and hard-sought nirvana of the particle physics community. From the theoretical side, it is the main challenge to provide a consistent and model-independent tool for comparing the bounds and reach of the diverse experiments. We propose a first complete classification of minimal consistent Dark Matter models, which provides the missing link between experiments and top-down models. Consistency is achieved by imposing renormalisability and invariance under the full Standard Model symmetries. We apply this paradigm to fermionic Dark multiplets with up to one mediator. We also reconsider the one-loop contributions to direct detection, including the relevant effect of (small) mass splits in the Dark multiplet. Our work highlights the presence of unexplored viable models, and paves the way for the ultimate systematic hunt for the Dark…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDark Matter and Cosmic Phenomena · Atomic and Subatomic Physics Research · Quantum Mechanics and Applications
