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

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)

Abstract
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.

Title of program: TRolke version 2.0
Catalogue Id: AEFT_v1_0

Nature of problem
The problem is to calculate a frequentist confidence interval on the parameter of a Poisson process with statistical or systematic uncertainties in signal efficiency or background.

Versions of this program held in the CPC repository in Mendeley Data
AEFT_v1_0; TRolke version 2.0; 10.1016/j.cpc.2009.11.001