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Strength in numbers: optimal and scalable combination of LHC new-physics searches
by Jack Y. Araz, Andy Buckley, Benjamin Fuks, Humberto Reyes-Gonzalez, Wolfgang Waltenberger, Sophie L. Williamson, Jamie Yellen
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Submission summary
| Authors (as registered SciPost users): | Jack Araz · Andy Buckley · Benjamin Fuks · Humberto Reyes-González · Wolfgang Waltenberger |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2209.00025v3 (pdf) |
| Code repository: | https://gitlab.com/t-a-c-o/taco_code |
| Date accepted: | 2023-01-26 |
| Date submitted: | 2022-12-23 20:45 |
| Submitted by: | Reyes-González, Humberto |
| Submitted to: | SciPost Physics |
| Ontological classification | |
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| Academic field: | Physics |
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Abstract
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.
Published as SciPost Phys. 14, 077 (2023)
Reports on this Submission
Report #1 by Andrew Fowlie (Referee 1) on 2023-1-2 (Invited Report)
Report
I would like to thank the authors for considering my comments and addressing my concerns. I now strongly recommend publication.