To profile or to marginalize - A SMEFT case study

Brivio, Ilaria; Bruggisser, Sebastian; Elmer, Nina; Geoffray, Emma; Luchmann, Michel; Plehn, Tilman

doi:10.21468/SciPostPhys.16.1.035

SciPost Physics

To profile or to marginalize - A SMEFT case study

Ilaria Brivio, Sebastian Bruggisser, Nina Elmer, Emma Geoffray, Michel Luchmann, Tilman Plehn

SciPost Phys. 16, 035 (2024) · published 29 January 2024

doi: 10.21468/SciPostPhys.16.1.035
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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.

TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.16.1.035
TI  - To profile or to marginalize - A SMEFT case study
PY  - 2024/01/29
UR  - https://scipost.org/SciPostPhys.16.1.035
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 16
IS  - 1
SP  - 035
A1  - Brivio, Ilaria
AU  - Bruggisser, Sebastian
AU  - Elmer, Nina
AU  - Geoffray, Emma
AU  - Luchmann, Michel
AU  - Plehn, Tilman
AB  - 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.
ER  -

@Article{10.21468/SciPostPhys.16.1.035,
	title={{To profile or to marginalize - A SMEFT case study}},
	author={Ilaria Brivio and Sebastian Bruggisser and Nina Elmer and Emma Geoffray and Michel Luchmann and Tilman Plehn},
	journal={SciPost Phys.},
	volume={16},
	pages={035},
	year={2024},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.16.1.035},
	url={https://scipost.org/10.21468/SciPostPhys.16.1.035},
}

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