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

Journal of High Energy Physics

Regular Article - Theoretical Physics

Giacomo Cacciapaglia¹  Daniel Locke²  Alexander Belyaev³  Alexander Pukhov⁴ 

[1] Institut de Physique des Deux Infinis (IP2I) de Lyon, CNRS/IN2P3, UMR5822, F-69622, Villeurbanne Cedex, France;Université de Lyon, Université Claude Bernard Lyon 1, F-69001, Lyon, France;School of Physics and Astronomy, University of Southampton, Highfield, SO17 1BJ, Southampton, U.K.;School of Physics and Astronomy, University of Southampton, Highfield, SO17 1BJ, Southampton, U.K.;Particle Physics Department, Rutherford Appleton Laboratory, Chilton, OX11 0QX, Didcot, Oxon, U.K.;Skobeltsyn Inst. of Nuclear Physics, Moscow State Univ., 119992, Moscow, Russia;

关键词: Models for Dark Matter;    Particle Nature of Dark Matter;   

DOI  :  10.1007/JHEP10(2022)014

 received in 2022-04-05, accepted in 2022-09-16,  发布年份 2022

来源: Springer

【 摘 要 】

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.

【 授权许可】

Unknown   
© The Author(s) 2022

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