We assess the impact of top quark production at the LHC on global analyses of parton distributions (PDFs) and of Wilson coefficients in the SMEFT, both separately and in the framework of a joint interpretation. We consider the broadest top quark dataset to date containing all available measurements based on the full Run II luminosity. First, we determine the constraints that this dataset provides on the large-x gluon PDF and study its consistency with other gluon-sensitive measurements. Second, we carry out a SMEFT interpretation of the same dataset using state-of-the-art SM and EFT theory calculations, resulting in bounds on 25 Wilson coefficients modifying top quark interactions. Subsequently, we integrate the two analyses within the SIMUnet approach to realise a simultaneous determination of the SMEFT PDFs and the EFT coefficients and identify regions in the parameter space where their interplay is most phenomenologically relevant. We also demonstrate how to separate eventual BSM signals from QCD effects in the interpretation of top quark measurements at the LHC. 

/pdf/the-top-quark-legacy-of-the-lhc-run-ii-for-pdf-and-smeft-1ej21f76.pdf

The top quark legacy of the LHC Run II for PDF and SMEFT analyses

A bstract Recasting phenomenological Lagrangians in terms of SM effective field theory (SMEFT) provides a valuable means of connecting potential BSM physics at momenta well above the electroweak scale to experimental signatures at lower energies. In this work we jointly fit the Wilson coefficients of SMEFT operators as well as the PDFs in an extension of the CT18 global analysis framework, obtaining self-consistent constraints to possible BSM physics effects. Global fits are boosted with machine-learning techniques in the form of neural networks to ensure efficient scans of the full PDF+SMEFT parameter space. We focus on several operators relevant for top-quark pair and jet production at hadron colliders and obtain constraints on the Wilson coefficients with Lagrange Multiplier scans. We find mild correlations between the extracted Wilson coefficients, PDFs, and other QCD parameters, and see indications that these correlations may become more prominent in future analyses based on data of higher precision. This work serves as a new platform for joint analyses of SM and BSM physics based on the CTEQ-TEA framework. 

/pdf/simultaneous-cteq-tea-extraction-of-pdfs-and-smeft-i7e4hsnb.pdf

Simultaneous CTEQ-TEA extraction of PDFs and SMEFT parameters from jet and $$ t\overline{t} $$ data

We present the calculation of the contribtuions from the chromomagnetic and four-top-quark-operators within Standard Model Effective Field Theory (SMEFT) to Higgs boson pair production in gluon fusion, combined with full NLO QCD corrections. We study the effects of these operators on the total cross section and the invariant mass distribution of the Higgs-boson pair, at $\sqrt{s}=13.6$ TeV. These subleading operators are implemented in the generator ggHH_SMEFT, in the same Powheg-Box-V2 framework as the leading operators, such that their effects can be easily studied in a unified setup. 

Combining chromomagnetic and four-fermion operators with leading SMEFT operators for $gg\to hh$ at NLO QCD

We introduce a graph neural network architecture designed to extract novel phenomena in the Standard Model Effective Field Theory (SMEFT) context from LHC collision data. The proposed infrared- and collinear-safe architecture is sensitive to the angular orientation of radiation patterns in jets from hadronic decays of highly energetic massive particles. Equivariance with respect to rotations around the jet axis allows for extracting the information on the angular orientation decoupled from the jet substructure. We demonstrate the robustness of the approach and its potential for future probes of the SMEFT at the LHC through toy studies and with realistic event simulations of the WZ process in the semileptonic decay channel.

pdf/rotation-equivariant-graph-neural-network-for-learning-476xl8621z.pdf

Rotation-equivariant graph neural network for learning hadronic SMEFT effects

A bstract We present the calculation of the contribtuions from the chromomagnetic and four-top-quark-operators within Standard Model Effective Field Theory (SMEFT) to Higgs boson pair production in gluon fusion, combined with QCD corrections that are at NLO with full m t -dependence for the leading operators. We study the effects of these operators on the total cross section and the invariant mass distribution of the Higgs-boson pair, at $$\sqrt{s}$$ = 13 . 6 TeV. These subleading operators are implemented in the generator ggHH_SMEFT, in the same Powheg-Box-V2 framework as the leading operators, such that their effects can be easily studied in a unified setup. 

Combining chromomagnetic and four-fermion operators with leading SMEFT operators for gg → hh at NLO QCD

Theoretical interpretations of particle physics data, such as the determination of the Wilson coefficients of the Standard Model Effective Field Theory (SMEFT), often involve the inference of multiple parameters from a global dataset. Optimizing such interpretations requires the identification of observables that exhibit the highest possible sensitivity to the underlying theory parameters. In this work we develop a flexible open source framework, ML4EFT, enabling the integration of unbinned multivariate observables into global SMEFT fits. As compared to traditional measurements, such observables enhance the sensitivity to the theory parameters by preventing the information loss incurred when binning in a subset of final-state kinematic variables. Our strategy combines machine learning regression and classification techniques to parameterize high-dimensional likelihood ratios, using the Monte Carlo replica method to estimate and propagate methodological uncertainties. As a proof of concept we construct unbinned multivariate observables for top-quark pair and Higgs+$Z$ production at the LHC, demonstrate their impact on the SMEFT parameter space as compared to binned measurements, and study the improved constraints associated to multivariate inputs. Since the number of neural networks to be trained scales quadratically with the number of parameters and can be fully parallelized, the ML4EFT framework is well-suited to construct unbinned multivariate observables which depend on up to tens of EFT coefficients, as required in global fits. 

/pdf/unbinned-multivariate-observables-for-global-smeft-analyses-vqir1r0q.pdf

Unbinned multivariate observables for global SMEFT analyses from machine learning

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Unbinned multivariate observables for global SMEFT analyses from machine learning