Bayesian selection of sign μ within mSUGRA in global fits including WMAP5 results

2008/07/31 by Farhan Feroz, F. Feroz, B. C. Allanach +7
Mathematics · Physics and Astronomy · #Artificial intelligence #Bayesian probability #Cold dark matter #Computer science #Consistency (knowledge bases) #Cosmology and Gravitation Theories #Dark matter #Data mining #Materials science #Mathematical analysis #Mathematics #Measure (data warehouse) #Neutrino Physics Research #Particle physics #Particle physics theoretical and experimental studies #Physics #Range (aeronautics) #Selection (genetic algorithm) #Sign (mathematics) #Statistics #Superpartner #Supersymmetry #hep-ph

paper · pdf · doi:10.1088/1126-6708/2008/10/064

published as JHEP 10 (2008) 064 · 28 pages, 8 figures, minor revision, accepted for publication in JHEP

openalex publication_date 2008/10/15 · arxiv created 2008/10/22 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/06

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_DMh².

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