Minimal Consistent models for systematic Dark Matter exploration
Alexander Belyaev, Giacomo Cacciapaglia, Daniel Locke

TL;DR
This paper proposes a systematic classification framework for minimal consistent dark matter models to enhance the exploration of dark matter through collider and non-collider experiments.
Contribution
It introduces a novel classification scheme for minimal dark matter models, providing a structured approach for future experimental and theoretical studies.
Findings
Developed a comprehensive classification of minimal dark matter models
Established a framework for systematic dark matter exploration
Facilitated comparison of experimental results within a unified model space
Abstract
Dark Matter searches in collider and non-collider experiments requires systematic and consistent approach. We suggest and perform classification of Minimal Consistent Dark Matter models which are aimed to create a solid framework for Dark Matter exploration.
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Taxonomy
TopicsDark Matter and Cosmic Phenomena · Computational Physics and Python Applications · Particle physics theoretical and experimental studies
