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

2009/07/31 by Johannes Lundberg, J. Lundberg, J. Conrad +4 · 40 citations
Decision Sciences · Mathematics · Medicine · Physics and Astronomy · #Computer science #Econometrics #Mathematics #Medical Imaging Techniques and Applications #Poisson distribution #Process (computing) #Radiation Detection and Scintillator Technologies #SIGNAL (programming language) #Scientific Computing and Data Management #Statistics #hep-ex #hep-ph #physics.data-an

paper · pdf · doi:10.1016/j.cpc.2009.11.001

published in Computer Physics Communications 181(3), 683-686 (Elsevier BV) · 18 pages, 1 figure

openalex publication_date 2009/11/12 · arxiv created 2010/01/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

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.

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