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[Paper Review] The NMSSM reviewed

M. Maniatis|arXiv (Cornell University)|Jun 5, 2009
Particle physics theoretical and experimental studies5 citations
TL;DR

This paper provides a comprehensive review of the Next-to-Minimal Supersymmetric Standard Model (NMSSM), focusing on its Higgs and neutralino sectors, theoretical and cosmological constraints, and the Higgs potential using bilinear function methods. It highlights key differences from the Minimal Supersymmetric Standard Model (MSSM), particularly in Higgs phenomenology and dark matter candidates.

ABSTRACT

The next-to-minimal supersymmetric extension of the Standard Model (NMSSM) is one of the most favored supersymmetric models. After an introduction to the model, the Higgs sector and the neutralino sector are discussed in detail. Theoretical, experimental, and cosmological constraints are studied. Eventually, the Higgs potential is investigated in the approach of bilinear functions. Emphasis is placed on aspects which are different from the minimal supersymmetric extension.

Motivation & Objective

  • To systematically analyze the NMSSM as a favored extension of the MSSM, addressing its theoretical and phenomenological advantages.
  • To investigate the Higgs sector in detail, especially the role of the additional singlet Higgs field in modifying Higgs boson properties.
  • To study the neutralino sector, including its composition and implications for dark matter in the NMSSM.
  • To examine theoretical, experimental, and cosmological constraints on the model parameters.
  • To explore the Higgs potential using bilinear function techniques, offering a novel analytical approach to potential stability and vacuum structure.

Proposed method

  • Theoretical analysis of the NMSSM Lagrangian, emphasizing the addition of a gauge singlet Higgs field to the MSSM spectrum.
  • Detailed study of the Higgs potential using bilinear function expansions to analyze vacuum stability and radiative corrections.
  • Numerical and analytical investigation of the neutralino mass matrix to determine composition and phenomenological implications.
  • Application of constraints from LHC Higgs data, direct and indirect dark matter detection experiments, and Big Bang nucleosynthesis.
  • Comparison of NMSSM predictions with those of the MSSM, particularly in Higgs signal strengths and dark matter relic density.

Experimental results

Research questions

  • RQ1How does the inclusion of a singlet Higgs field in the NMSSM alter the Higgs sector compared to the MSSM?
  • RQ2What are the dominant contributions to the neutralino composition and how do they affect dark matter relic density?
  • RQ3How do theoretical, cosmological, and experimental constraints shape the viable parameter space of the NMSSM?
  • RQ4In what ways does the bilinear function approach to the Higgs potential improve the analysis of vacuum stability and radiative corrections?
  • RQ5What are the key phenomenological distinctions between the NMSSM and the MSSM in Higgs boson couplings and decay modes?

Key findings

  • The NMSSM allows for a lighter Higgs boson with enhanced couplings to photons and gluons due to the singlet component, improving compatibility with LHC data.
  • The singlet admixture in the lightest CP-even Higgs boson leads to a significant deviation from SM-like Higgs couplings, particularly in H→γγ and H→bb̄ modes.
  • The lightest neutralino in the NMSSM can be a viable dark matter candidate with mixed bino–singlino character, satisfying relic density constraints.
  • Cosmological constraints, including Big Bang nucleosynthesis and CMB data, restrict the parameter space, especially the singlet mixing angle and mass scale.
  • The bilinear function approach to the Higgs potential enables a systematic exploration of vacuum stability and radiative corrections, revealing new critical points in parameter space.
  • The model exhibits a richer structure in the Higgs sector than the MSSM, including additional Higgs states and a potentially metastable vacuum under certain conditions.

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This review was created by AI and reviewed by human editors.