Strength in numbers: optimal and scalable combination of LHC new-physics searches
| Autor: | Araz, Jack Y., Buckley, Andy, Fuks, Benjamin, Reyes-Gonzalez, Humberto, Waltenberger, Wolfgang, Williamson, Sophie L., Yellen, Jamie |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | SciPost Phys. 14, 077 (2023) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.21468/SciPostPhys.14.4.077 |
| Popis: | 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. Comment: 35 pages, 15 figures. Updated version for SciPost submission |
| Databáze: | arXiv |
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