Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0

Details Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0 Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0

2009-07-20

arxiv.org/abs/0907.3450v2

Comput.Phys.Commun.181:683-686,2010

Physics

Data Analysis, Statistics and Probability

18 pages, 1 figure

Scientific paper

10.1016/j.cpc.2009.11.001

A C++ class was written for the calculation of frequentist confidence intervals using the profile likelihood method. Seven combinations of Binomial, Gaussian, Poissonian and Binomial uncertainties are implemented. The package provides routines for the calculation of upper and lower limits, sensitivity and related properties. It also supports hypothesis tests which take uncertainties into account. It can be used in compiled C++ code, in Python or interactively via the ROOT analysis framework.

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