SFitter: Determining supersymmetric parameters

If supersymmetry (or similar complex models) is found at the LHC, the goal for all colliders over the coming decades will be to extract its fundamental parameters 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 hig hdimensional parameter space we show how we can find the best–fit paramete r values and determine their errors. Beyond a single best–fit point we illustra te how distinct secondary minima occur in complex parameter spaces. In cases where there are flat dimensions in the likelihood we comment on the benefits and limitations of marginalizing over additional dimensions.