Sven Kreiss (New York University (US))
RooStats is a project providing advanced statistical tools required for the analysis of LHC data, with emphasis on discoveries, confidence intervals, and combined measurements in the both the Bayesian and Frequentist approaches. The tools are built on top of the RooFit data modeling language and core ROOT mathematics libraries and persistence technology. These tools have been developed in collaboration with the LHC experiments and used by them to produce numerous physics results, such as the combination of ATLAS and CMS Higgs searches that resulted in a model with more than 200 parameters. We will review new developments which have been included in RooStats and the performance optimizations, required to cope with such complex models used by the LHC experiments. We will show as well the parallelization capability of these statistical tools using multiple-processors via PROOF.
Dr Alfio Lazzaro (Universita degli Studi di Milano-Universita e INFN) Gennadiy Kukartsev (Brown University (US)) Giovanni Petrucciani (Univ. of California San Diego (US)) Dr Gregory Alfred Schott (KIT - Karlsruhe Institute of Technology (DE)) Kyle Stuart Cranmer (New York University (US)) Wouter Verkerke (NIKHEF (NL))