To profile or to marginalize - a SMEFT case study
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...
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| Main Authors: | , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
29 January 2024
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| In: |
SciPost physics
Year: 2024, Volume: 16, Issue: 1, Pages: 1-39 |
| ISSN: | 2542-4653 |
| DOI: | 10.21468/SciPostPhys.16.1.035 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.21468/SciPostPhys.16.1.035 Verlag, lizenzpflichtig, Volltext: https://scipost.org/10.21468/SciPostPhys.16.1.035 
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| Author Notes: | Ilaria Brivio, Sebastian Bruggisser, Nina Elmer, Emma Geoffray, Michel Luchmann and Tilman Plehn |
| Summary: | 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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| Item Description: | Gesehen am 17.06.2024 |
| Physical Description: | Online Resource |
| ISSN: | 2542-4653 |
| DOI: | 10.21468/SciPostPhys.16.1.035 |