Aug 23 – 28, 2010
Europe/Berlin timezone

Exploring the SUSY Landscape: A New Bayesian Approach

Aug 23, 2010, 5:20 PM
Gr. Hörsaal Mathematik (Bonn)

Gr. Hörsaal Mathematik


Nussallee 12


Sezen Sekmen (Florida State University)


Supersymmetry (SUSY), with its multi-parameter space, has been a popular playground for many years. But now that the Large Hadron Collider (LHC) is operational, the goal is to determine whether this playground has anything to do with reality. In principle, the Bayesian approach provides a well-founded, coherent, way to perform such studies. However, such studies face a common problem: the construction of a suitable prior on the parameter space, from basic principles. Current studies place flat or logarithmic priors on the SUSY parameter spaces, but it is well-established that such priors can lead to pathological results in multi-parameter spaces. Here we propose a solution that starts with a reference prior and constructs the signal posterior density for a single count analysis. Since the signal is a function of the SUSY parameters, this posterior density induces some prior on that space. We argue that every SUSY model point, consistent with a given signal, that is, every "look-alike", be assigned the same prior density. We show that this is sufficient to construct a well-defined prior that permits the recursive inclusion of other measurements using standard Bayesian methods and therefore a prior than can be used for the systematic exploration of the SUSY landscape at the LHC.

Primary authors

Harrison Prosper (Florida State University) Maria Spiropulu (CERN/Caltech) Maurizio Pierini (CERN) Sezen Sekmen (Florida State University)

Presentation materials