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, sensitivi...

Descripción completa

Detalles Bibliográficos
Autores principales: Lundberg, J., Conrad, J., Rolke, W., Lopez, A.
Lenguaje:eng
Publicado: 2009
Materias:
physics.data-an
Acceso en línea:https://dx.doi.org/10.1016/j.cpc.2009.11.001
http://cds.cern.ch/record/1191778
_version_ 1780916623033499648
author Lundberg, J.
Conrad, J.
Rolke, W.
Lopez, A.
author_facet Lundberg, J.
Conrad, J.
Rolke, W.
Lopez, A.
author_sort Lundberg, J.
collection CERN
description 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.
id cern-1191778
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2009
record_format invenio
spelling cern-11917782023-10-04T06:37:19Zdoi:10.1016/j.cpc.2009.11.001http://cds.cern.ch/record/1191778engLundberg, J.Conrad, J.Rolke, W.Lopez, A.Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0physics.data-anA 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.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.arXiv:0907.3450oai:cds.cern.ch:11917782009-07-21
spellingShingle physics.data-an
Lundberg, J.
Conrad, J.
Rolke, W.
Lopez, A.
Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0
title Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0
title_full Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0
title_fullStr Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0
title_full_unstemmed Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0
title_short Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0
title_sort limits, discovery and cut optimization for a poisson process with uncertainty in background and signal efficiency: trolke 2.0
topic physics.data-an
url https://dx.doi.org/10.1016/j.cpc.2009.11.001
http://cds.cern.ch/record/1191778
work_keys_str_mv AT lundbergj limitsdiscoveryandcutoptimizationforapoissonprocesswithuncertaintyinbackgroundandsignalefficiencytrolke20
AT conradj limitsdiscoveryandcutoptimizationforapoissonprocesswithuncertaintyinbackgroundandsignalefficiencytrolke20
AT rolkew limitsdiscoveryandcutoptimizationforapoissonprocesswithuncertaintyinbackgroundandsignalefficiencytrolke20
AT lopeza limitsdiscoveryandcutoptimizationforapoissonprocesswithuncertaintyinbackgroundandsignalefficiencytrolke20

Ejemplares similares