Bayesian analysis and naturalness of (Next-to-)Minimal Supersymmetric Models

TL;DR

This paper compares the naturalness of semi-constrained NMSSM and CMSSM models using Bayesian priors and fine-tuning measures, highlighting the importance of priors in assessing hierarchy and superpartner masses.

Contribution

It demonstrates that naturalness priors align with traditional fine-tuning measures, providing a Bayesian foundation for evaluating supersymmetric models' plausibility.

Findings

Naturalness priors agree with fine-tuning measures.

Bayesian analysis offers a rigorous hierarchy problem perspective.

Supports the idea of relatively light superpartners.

Abstract

The Higgs boson discovery stirred interest in next-to-minimal supersymmetric models, due to the apparent fine-tuning required to accommodate it in minimal theories. To assess their naturalness, we compare fine-tuning in a $\mathbb{Z}_3$ conserving semi-constrained Next-to-Minimal Supersymmetric Standard Model (NMSSM) to the constrained MSSM (CMSSM). We contrast popular fine-tuning measures with naturalness priors, which automatically appear in statistical measures of the plausibility that a given model reproduces the weak scale. Our comparison shows that naturalness priors provide valuable insight into the hierarchy problem and rigorously ground naturalness in Bayesian statistics. For the CMSSM and semi-constrained NMSSM we demonstrate qualitative agreement between naturalness priors and popular fine tuning measures. Thus, we give a clear plausibility argument that favours relatively light superpartners.