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To Profile or To Marginalize -- A SMEFT Case Study

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arxiv 2208.08454 v3 pith:ZM3XI5XA submitted 2022-08-17 hep-ph

To Profile or To Marginalize -- A SMEFT Case Study

Ilaria Brivio , Sebastian Bruggisser , Nina Elmer , Emma Geoffray , Michel Luchmann , Tilman Plehn This is my paper
classification hep-ph
keywords profileanalysesbayesianframeworkglobalkinematiclikelihoodmarginalization
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Global SMEFT analyses have become a key interpretation framework for LHC physics, quantifying how well a large set of kinematic measurements agrees with the Standard Model. This agreement is encoded in measured Wilson coefficients and their uncertainties. A technical challenge of global analyses are correlations. We compare, for the first time, results from a profile likelihood and a Bayesian marginalization for a given data set with a comprehensive uncertainty treatment. Using the validated Bayesian framework we analyse a series of new kinematic measurements. For the updated dataset we find and explain differences between the marginalization and profile likelihood treatments.

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