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Updated: Dec 29, 2025

Setting Limits on Supersymmetry Using Simplified Models
Published on: November 15, 2013
Status of the scalar singlet dark matter model
, Peter Athron1,2, Csaba Balázs1,2
11School of Physics and Astronomy, Monash University, Melbourne, VIC 3800 Australia.
This study explores a simple dark matter model using the GAMBIT package. Viable solutions exist for scalar singlet dark matter, particularly at higher masses, though some regions require fine-tuning.
Area of Science:
- Particle Physics
- Cosmology
- Astrophysics
Background:
- The nature of dark matter remains one of the most significant unsolved problems in physics.
- A simple model for dark matter involves an additional neutral scalar particle stabilized by a symmetry.
Purpose of the Study:
- To perform global Bayesian and frequentist fits of the neutral scalar dark matter model.
- To identify viable parameter spaces for this dark matter model by combining various experimental constraints.
Main Methods:
- Utilized the GAMBIT (General Analysis of Minimal Beyond-Standard-Model Integrations and Tools) package.
- Employed four independent samplers for global model fitting.
- Varied singlet mass, coupling, and 13 nuisance parameters, including nuclear uncertainties and local dark matter density.
Main Results:
- Identified viable solutions for scalar singlet dark matter at couplings of order unity.
- Viable parameter spaces exist for singlet masses between the Higgs mass and ~300 GeV, and above ~1 TeV.
- The scalar singlet can constitute all dark matter for masses above ~1 TeV.
- A low-mass resonance region (~half Higgs mass) is viable under frequentist analysis but considered fine-tuned in Bayesian analysis.
Conclusions:
- The neutral scalar dark matter model remains viable across different mass ranges and experimental constraints.
- The high-mass region (>1 TeV) is a robust parameter space where the scalar singlet can be the sole dark matter candidate.
- While a low-mass resonance region is statistically viable, it presents fine-tuning challenges from a Bayesian perspective.
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