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To Profile or To Marginalize -- A SMEFT Case Study
by Ilaria Brivio, Sebastian Bruggisser, Nina Elmer, Emma Geoffray, Michel Luchmann, Tilman Plehn
This Submission thread is now published as
Submission summary
| Authors (as registered SciPost users): | Ilaria Brivio · Nina Elmer · Emma Geoffray · Tilman Plehn |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2208.08454v3 (pdf) |
| Date accepted: | 2024-01-10 |
| Date submitted: | 2024-01-02 20:08 |
| Submitted by: | Elmer, Nina |
| Submitted to: | SciPost Physics |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approach: | Phenomenological |
Abstract
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.
List of changes
We would like to thank the referees for their comments and provide an updated version addressing their requests. A detailed list of changes can be found in our replies to the referees.
Published as SciPost Phys. 16, 035 (2024)