Minimal consistent Dark Matter models for systematic experimental characterisation: fermion Dark Matter - CNRS - Centre national de la recherche scientifique Accéder directement au contenu
Article Dans Une Revue Journal of High Energy Physics Année : 2022

Minimal consistent Dark Matter models for systematic experimental characterisation: fermion Dark Matter

Résumé

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, abbreviated as MCDMs, that are defined by one Dark Matter weak multiplet with up to one mediator multiplet. This classification 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 the fermionic Dark Matter case. 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 Matter particle.

Dates et versions

hal-03616948 , version 1 (23-03-2022)

Identifiants

Citer

Alexander Belyaev, Giacomo Cacciapaglia, Daniel Locke, Alexander Pukhov. Minimal consistent Dark Matter models for systematic experimental characterisation: fermion Dark Matter. Journal of High Energy Physics, 2022, 10, pp.014. ⟨10.1007/JHEP10(2022)014⟩. ⟨hal-03616948⟩
30 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More