发表机构
CERN; The University of Glasgow; INFN Gruppo Collegato di Udine, Sezione di Trieste（欧洲核子研究中心; 格拉斯哥大学; 意大利国家核物理学院乌迪内协作组的里雅斯特分部）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出用BLUE方法同时组合感兴趣参数与系统不确定性参数，通过示例和伪实验证明包含干扰参数可提高精度，并指出Convino软件近似似然组合低估不确定性，方法已实现于开源工具Combiner。
AI 中文摘要
将来自不同测量的同一物理参数的估计值进行组合，可以提高参数确定的精度和稳健性。现代粒子物理测量通常使用似然拟合进行，其中包含感兴趣的物理参数以及代表系统不确定性的干扰参数。在高统计量分析中，例如大型强子对撞机上的许多典型分析，这些干扰参数可以在似然拟合中被约束。我们描述了如何将最佳线性无偏估计器组合方法应用于感兴趣参数的估计和干扰参数的估计。我们通过具体的示例组合表明，包含干扰参数可以提高感兴趣参数的精度。我们通过伪实验表明，组合报告的不确定性是可靠的，而先前出版物中提出并在Convino软件中实现的近似似然组合报告了低估的不确定性。该方法已在开源软件工具Combiner中实现。
英文摘要
Combining estimates of the same physics parameter obtained from different measurements improves the precision and robustness of the parameter determination. Modern particle physics measurements are often performed using likelihood fits that include the physics parameter(s) of interest together with nuisance parameters representing systematic uncertainties. In high-statistics analyses, typical of many analyses at the Large Hadron Collider, these nuisance parameters can be constrained in the likelihood fits. We describe how the Best Linear Unbiased Estimator method for combinations can be applied to both the estimates of the parameters of interest and the estimates of the nuisance parameters. We show with concrete example combinations that including the nuisance parameters can improve the precision on the parameters of interest. We show with pseudo-experiments that the uncertainty reported by the combination is reliable and that the approximate likelihood combination proposed in a previous publication and implemented in the Convino software reports an underestimated uncertainty. The method is implemented in an open-source software tool, Combiner.
Comments13 pages, 5 figures, 4 tables, submitted to EPJC
