Fitting the Phenomenological MSSM

TL;DR

This paper presents the first comprehensive Bayesian global fit of the pMSSM, analyzing 25 parameters with current collider and dark matter data to infer properties like sparticle masses and dark matter detection prospects.

Contribution

It introduces a robust, statistically convergent Bayesian fitting method for the pMSSM using nested sampling, exploring parameter space without restrictive high-scale assumptions.

Findings

Inferred lightest Higgs mass is approximately prior-independent.

Provides constraints on sparticle masses and dark matter properties.

Assesses dark matter detection prospects based on current data.

Abstract

We perform a global Bayesian fit of the phenomenological minimal supersymmetric standard model (pMSSM) to current indirect collider and dark matter data. The pMSSM contains the most relevant 25 weak-scale MSSM parameters, which are simultaneously fit using `nested sampling' Monte Carlo techniques in more than 15 years of CPU time. We calculate the Bayesian evidence for the pMSSM and constrain its parameters and observables in the context of two widely different, but reasonable, priors to determine which inferences are robust. We make inferences about sparticle masses, the sign of the $\mu$ parameter, the amount of fine tuning, dark matter properties and the prospects for direct dark matter detection without assuming a restrictive high-scale supersymmetry breaking model. We find the inferred lightest CP-even Higgs boson mass as an example of an approximately prior independent observable. This analysis constitutes the first statistically convergent pMSSM global fit to all current data.