Speaker
Peter Schichtel
(Heidelberg University)
Description
The Neyman-Pearson lemma states that the likelihood-ratio is the most powerful estimator for hyposthesis testing. Given a signal and a background model our tool MadMax computes the log-likelihood distributions of the signal and background hypotheses for a given luminosity at the LHC. From these we calculate the corresponding gaussian significance to quantify the distance of the two hypotheses. The computation is fully differential which allows to plot the significance as function of any observable. This turns MadMax into a powerfull tool which can guide analysis strategies and the development of specialized search tools like Higgs or Top-taggers. Finally, our tool is implemented into the MadGraph5 framework which allows easy usage.
Primary authors
Daniel Wiegand
(Pittsburgh University)
Martin Jankowiak
(Heidelberg Univeristy)
Peter Schichtel
(Heidelberg University)
Tilman Plehn
(Heidelberg University)
