Description
SFitter is a tool to map many measurements onto a high-dimensional parameter space. It has been applied to reconstruct Higgs Yukawa couplings from (simulated) LHC data and the parameters of a TeV-scale MSSM from both LHC and linear collider data. Thereby, strong correlations between different measurements or degenerate best solutions do not pose any problem and errors are fully propagated. Starting from a fully-dimensional log-likelihood map, SFitter outputs one- and two-dimensional distributions using both Bayesian and Frequentist techniques, as well as a list of best-fitting points.
Organised by
N. Mahmoudi, J. Winter