Speaker
Lorenzo Moneta
(CERN)
Description
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 modelling language, where mathematical concepts such as variables, (probability density) functions and integrals are represented as C++ objects. RooFit and RooStats are built on top of mathematics libraries and persistence technology of the ROOT framework.
These advanced statistical tools have been developed in collaboration with the LHC experiments and used by them to produce numerous physics results, the discovery of the the Higgs boson by ATLAS and CMS Higgs, using models with more than 1000 parameters. We will present the tools currently used by the LHC experiments to estimate confidence intervals, exclusion limits and discovery significances such as those based on frequentist statistics or the asymptotic properties of the likelihood function. We will also review the new developments which have been included in RooStats and the performance optimizations, required to cope with such complex models used by the LHC experiments.
Primary author
Lorenzo Moneta
(CERN)
Co-authors
Gena Kukartsev
(Brown University (US))
Kyle Stuart Cranmer
(New York University (US))
Sven Kreiss
(New York University (US))
Wouter Verkerke
(NIKHEF (NL))
