Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0
Data Analysis, Statistics and Probability
2010-01-21 v2 High Energy Physics - Experiment
High Energy Physics - Phenomenology
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.
Keywords
Cite
@article{arxiv.0907.3450,
title = {Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0},
author = {J. Lundberg and J. Conrad and W. Rolke and A. Lopez},
journal= {arXiv preprint arXiv:0907.3450},
year = {2010}
}
Comments
18 pages, 1 figure