Bayesian forecasts from Dark Matter Direct Detection to LHC

Who: Giorgio Arcadi (QFT Uni Göttingen)
When: Monday, January 21, 2013 at 14:15
Where: U49


The complementarity between different strategies of Dark Matter search is a powerful tool for identifying the DM microscopic properties and its importance is growing in perspective of the next future update of LHC and of the onset of next generation Dark Matter Direct Detection (DD) experiments. I will discuss a strategy for combining DD and collider searches. Adopting as working framework the Minimal Supersymmetric Standard Model (MSSM), a method for translating the information encoded in an hypothetically discovered DD signal into classes of expected signals at LHC will be proposed. For illustrative purposes I will discuss the application of this method to some benchmark scenarios. The result obtained are regarded as a first step towards the development of a strategy to systematically translate a direct detection signal into a prediction for the LHC.



Slides from the talk