Statistical approach to Higgs couplings in the standard model effective field theory
Christopher W. Murphy

TL;DR
This paper applies regularized linear regression to fit Higgs boson data within the SMEFT framework, finding the SM as the best explanation and exploring implications for Higgs properties and future measurements.
Contribution
It introduces a regularized linear regression approach to SMEFT parameter fitting, providing a model-independent analysis of Higgs data and constraints on new physics.
Findings
SMEFT predicts Higgs width consistent with SM
LHC experiments are sensitive to non-resonant double Higgs production
Constraints support low-scale new physics for electroweak baryogenesis
Abstract
We perform a parameter fit in the Standard Model Effective Field Theory (SMEFT) with an emphasis on using regularized linear regression to tackle the issue of the large number of parameters in the SMEFT. In regularized linear regression a positive definite function of the parameters of interest is added to the usual cost function. A cross-validation is performed to try to determine the optimal value of the regularization parameter to use, but it selects the Standard Model (SM) as the best model to explain the measurements. Nevertheless as proof of principle of this technique we apply it to fitting Higgs boson signal strengths in SMEFT, including the latest Run-2 results. Results are presented in terms of the eigensystem of the covariance matrix of the least squares estimators as it has a degree model-independent to it. We find several results in this initial work: the SMEFT predicts the…
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