SFitter: Determining Supersymmetric Parameters

If supersymmetry (or a similar complex phenomenon) is found at the LHC, the goal for all colliders over the coming decades will be to extract the fundamental parameters of an underlying model from the measurements. Dedicated state-of-the-art tools will be necessary to link a wealth of measurements to an e.g. 20-dimensional MSSM parameter space. Starting from a general log- likelihood function of this high-dimensional parameter space we show how we can nd the best-fit parameter values and determine their errors. Beyond a single best-fit point we illustrate how distinct secondary minima occur in complex parameter spaces. In cases where there are at dimensions in the likelihood we comment on the bene ts and limitations of marginalizing over additional dimensions.