The Simplified Likelihood Framework

Abstract

We discuss the simplified likelihood framework as a systematic approximation scheme for experimental likelihoods such as those originating from LHC experiments. We develop the simplified likelihood from the Central Limit Theorem keeping the next-to-leading term in the large $N$ expansion to correctly account for asymmetries. Moreover, we present an efficient method to compute the parameters of the simplified likelihood from Monte Carlo simulations. The approach is validated using a realistic LHC-like analysis, and the limits of the approximation are explored. Finally, we discuss how the simplified likelihood data can be conveniently released in the HepData error source format and automatically built from it, making this framework a convenient tool to transmit realistic experimental likelihoods to the community.

arXiv comment24 pages, 6 figures. v2: Minor changes, references added, matches JHEP version. Reference code and demo available at https://gitlab.cern.ch/SimplifiedLikelihood/SLtools

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• Asymptotic formulae for likelihood-based tests of new physics
• HEPData: a repository for high energy physics data
• Searches for New Physics: Les Houches Recommendations for the Presentation of LHC Results
• Decoupling Theoretical Uncertainties from Measurements of the Higgs Boson
• The correlation matrix of Higgs rates at the LHC
• Taming systematic uncertainties at the LHC with the central limit theorem

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• Reinterpretation of LHC Results for New Physics: Status and Recommendations after Run 2
• Publishing statistical models: Getting the most out of particle physics experiments
• A SModelS interface for pyhf likelihoods
• The DNNLikelihood: enhancing likelihood distribution with Deep Learning
• SModelS v2.3: enabling global likelihood analyses
• Signal region combination with full and simplified likelihoods in MadAnalysis 5
• Spey: smooth inference for reinterpretation studies
• Global LHC constraints on electroweak-inos with SModelS v2.3
• Artificial Proto-Modelling: Building Precursors of a Next Standard Model from Simplified Model Results
• Simplified likelihoods using linearized systematic uncertainties
• Reporting Results in High Energy Physics Publications: a Manifesto
• Staying on Top of SMEFT-Likelihood Analyses