论文标题
剖面或边缘化 - SMEFT案例研究
To Profile or To Marginalize -- A SMEFT Case Study
论文作者
论文摘要
全球SMEFT分析已成为LHC物理学的关键解释框架，量化了大量运动学测量与标准模型一致的程度。该协议在测得的威尔逊系数及其不确定性中编码。全球分析的技术挑战是相关性。我们首次比较了特征可能性和贝叶斯边缘化的引起的，对于给定的数据集，具有全面的不确定性处理。使用经过验证的贝叶斯框架，我们分析了一系列新的运动测量。对于更新的数据集，我们发现并解释边缘化和轮廓似然处理之间的差异。
Global SMEFT analyses have become a key interpretation framework for LHC physics, quantifying how well a large set of kinematic measurements agrees with the Standard Model. This agreement is encoded in measured Wilson coefficients and their uncertainties. A technical challenge of global analyses are correlations. We compare, for the first time, results from a profile likelihood and a Bayesian marginalization for a given data set with a comprehensive uncertainty treatment. Using the validated Bayesian framework we analyse a series of new kinematic measurements. For the updated dataset we find and explain differences between the marginalization and profile likelihood treatments.
