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

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.Program summaryProgram title: TRolke version 2.0Catalogue identifier: AEFT_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEFT_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: MIT licenseNo. of lines in distributed program, including test data, etc.: 3431No. of bytes in distributed program, including test data, etc.: 21 789Distribution format: tar.gzProgramming language: ISO C++.Computer: Unix, GNU/Linux, Mac.Operating system: Linux 2.6 (Scientific Linux 4 and 5, Ubuntu 8.10), Darwin 9.0 (Mac-OS X 10.5.8).RAM:  ∼20 MB∼20 MBClassification: 14.13.External routines: ROOT (http://root.cern.ch/drupal/)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.Solution method: Profile likelihood method, AnalyticalRunning time:  <10−4<10−4 seconds per extracted limit.