Bayesian Selection of sign(mu) within mSUGRA in Global Fits Including WMAP5 Results

TL;DR

This paper uses Bayesian methods and the MultiNest technique to compare the likelihood of mu positive versus negative in the mSUGRA model, incorporating WMAP5 dark matter data, and finds moderate evidence favoring mu > 0.

Contribution

It applies Bayesian evidence and nested sampling to assess the sign of mu in mSUGRA, providing a quantitative comparison and exploring prior dependence.

Findings

Weak to moderate evidence for mu > 0 over mu < 0

Bayesian evidence ratios range from 6 to 61

Parameter distributions and model consistency are analyzed

Abstract

We study the properties of the constrained minimal supersymmetric standard model (mSUGRA) by performing fits to updated indirect data, including the relic density of dark matter inferred from WMAP5. In order to find the extent to which mu < 0 is disfavoured compared to mu > 0, we compare the Bayesian evidence values for these models, which we obtain straightforwardly and with good precision from the recently developed multi-modal nested sampling ('MultiNest') technique. We find weak to moderate evidence for the mu > 0 branch of mSUGRA over mu < 0 and estimate the ratio of probabilities to be P(mu > 0)/P(mu < 0) = 6-61 depending on the prior measure and range used. There is thus positive (but not overwhelming) evidence that mu > 0 in mSUGRA. The MultiNest technique also delivers probability distributions of parameters and other relevant quantities such as superpartner masses. We explore the dependence of our results on the choice of the prior measure used. We also use the Bayesian evidence to quantify the consistency between the mSUGRA parameter inferences coming from the constraints that have the largest effects: (g-2)_mu, BR(b -> s gamma) and cold dark matter (DM) relic density Omega_{DM}h^2.