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

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

J. Lundberg
(
- CERN
)
,
J. Conrad
(
- Stockholm U.
)
,
W. Rolke
(
- Puerto Rico U., Mayaguez
)
,
A. Lopez
(
- Puerto Rico U., Mayaguez
)

Jul, 2009

18 pages

Published in:

Comput.Phys.Commun. 181 (2010) 683-686

e-Print:

0907.3450 [physics.data-an]

DOI:

10.1016/j.cpc.2009.11.001

View in:

reference search45 citations

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Abstract: (arXiv)

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.

06.20.Dk
Confidence intervals
Hypothesis tests
Systematic uncertainties
Poisson statistics

References(6)

Figures(1)

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