论文标题
数量的强度：LHC新物理搜索的最佳和可扩展组合
Strength in numbers: optimal and scalable combination of LHC new-physics searches
论文作者
论文摘要
为了对LHC告诉我们的有关标准模型（BSM）以外的物理学的信息进行全面的看法，至关重要的是，可以将不同的BSM敏感分析结合在一起。但是通常，搜索分析在统计上不是正交的，因此进行全面的组合需要了解相同事件共同填充多个分析的信号区域的程度。我们提出了一种新型的随机方法，用于确定这种重叠程度和图形算法，以有效地找到信号区域的组合，而没有相互重叠，该组合优化了BSM模型横截面的预期上限。相对于单分析极限的排除功率的增益通过具有不同程度复杂性的模型证明，从简化模型到19维超对称模型。
To gain a comprehensive view of what the LHC tells us about physics beyond the Standard Model (BSM), it is crucial that different BSM-sensitive analyses can be combined. But in general, search analyses are not statistically orthogonal, so performing comprehensive combinations requires knowledge of the extent to which the same events co-populate multiple analyses' signal regions. We present a novel, stochastic method to determine this degree of overlap and a graph algorithm to efficiently find the combination of signal regions with no mutual overlap that optimises expected upper limits on BSM-model cross-sections. The gain in exclusion power relative to single-analysis limits is demonstrated with models with varying degrees of complexity, ranging from simplified models to a 19-dimensional supersymmetric model.
