Strength in numbers: Optimal and scalable combination of LHC new-physics searches
Strength in numbers: Optimal and scalable combination of LHC new-physics searches
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DOI：
10.21468/scipostphys.14.4.077
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发表时间：
2022-08
期刊：
影响因子：
5.5
通讯作者：
Jack Y. Araz;Andy Buckley;B. Fuks;H. Reyes-González;W. Waltenberger;S. L. Williamson;Jamie Yellen
中科院分区：
文献类型：
--
作者：
Jack Y. Araz;Andy Buckley;B. Fuks;H. Reyes-González;W. Waltenberger;S. L. Williamson;Jamie Yellen
为了全面了解大型强子对撞机告诉我们的标准模型（BSM）之外的物理，将不同的BSM敏感分析结合起来是至关重要的。但一般来说，搜索分析在统计上并不是正交的，因此执行综合组合需要了解相同事件共同填充多个分析信号区域的程度。我们提出了一种新的随机方法来确定这种重叠程度，并提出了一种图算法来有效地找到没有相互重叠的信号区域的组合，从而优化bsm模型截面的预期上限。从简化模型到19维超对称模型，用不同复杂程度的模型证明了相对于单一分析极限的排除能力的增益。
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.