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

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018) 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. Title of program: TRolke version 2.0 Catalogue Id: AEFT_v1_0 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. Versions of this program held in the CPC repository in Mendeley Data AEFT_v1_0; TRolke version 2.0; 10.1016/j.cpc.2009.11.001

本程序源自贝尔法斯特女王大学馆藏的CPC程序库（1969-2018）。
摘要：本程序开发了一个C++类，用于基于轮廓似然法（profile likelihood method）计算频率学派置信区间（frequentist confidence intervals）。程序实现了七种由二项分布（Binomial）、高斯分布（Gaussian）、泊松分布（Poissonian）不确定度构成的组合方案。该软件包提供了上下限、灵敏度及相关属性的计算例程，同时支持纳入不确定度的假设检验。本程序可在编译后的C++代码、Python环境中运行，也可通过ROOT分析框架（ROOT analysis framework）进行交互式操作。
程序名称：TRolke 2.0版
目录编号：AEFT_v1_0
问题本质：针对信号效率或本底存在统计或系统不确定度的泊松过程参数，计算其频率学派置信区间。
孟德莱数据（Mendeley Data）中CPC程序库存储的本程序版本信息：AEFT_v1_0；TRolke 2.0版；DOI：10.1016/j.cpc.2009.11.001