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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
Preprint Link:  (pdf)
Code repository:
Date accepted: 2023-01-26
Date submitted: 2022-12-23 20:45
Submitted by: Reyes-González, Humberto
Submitted to: SciPost Physics
Ontological classification
Academic field: Physics
  • High-Energy Physics - Experiment
  • High-Energy Physics - Phenomenology


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 on 2023-1-2 (Invited Report)


I would like to thank the authors for considering my comments and addressing my concerns. I now strongly recommend publication.

  • validity: high
  • significance: high
  • originality: high
  • clarity: good
  • formatting: excellent
  • grammar: excellent

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