论文标题
具有最佳EFT灵敏度的参数化分类器
Parametrized classifiers for optimal EFT sensitivity
论文作者
论文摘要
我们研究了基于统计学习的未上系数多元分析技术，用于在有效的现场理论框架中的LHC进行间接的新物理搜索。我们特别专注于具有完全松弛的衰减的高能$ ZW $生产，以不同程度的精炼型为QCD中的NLO。我们表明，与基于BINNED分析的当前投影相比，敏感性可以大大提高。如预期的那样，对于那些表现出复杂干扰标准模型幅度模式的操作员而言，增益尤其重要。发现最有效的方法是“二次分类器”方法，这是标准统计学习分类器的改进，其中差分横截面对EFT Wilson系数的二次依赖性是内置的，并将其纳入损耗函数。我们认为，基于严格的最佳概念，我们可以为$ zw $流程的近似分析描述建立一个二次分类器的性能几乎在统计上是最佳的。
We study unbinned multivariate analysis techniques, based on Statistical Learning, for indirect new physics searches at the LHC in the Effective Field Theory framework. We focus in particular on high-energy $ZW$ production with fully leptonic decays, modeled at different degrees of refinement up to NLO in QCD. We show that a considerable gain in sensitivity is possible compared with current projections based on binned analyses. As expected, the gain is particularly significant for those operators that display a complex pattern of interference with the Standard Model amplitude. The most effective method is found to be the "Quadratic Classifier" approach, an improvement of the standard Statistical Learning classifier where the quadratic dependence of the differential cross section on the EFT Wilson coefficients is built-in and incorporated in the loss function. We argue that the Quadratic Classifier performances are nearly statistically optimal, based on a rigorous notion of optimality that we can establish for an approximate analytic description of the $ZW$ process.
