Improved Constraints on Effective Top Quark Interactions using Edge Convolution Networks

Abstract

We explore the potential of Graph Neural Networks (GNNs) to improve the performance of high-dimensional effective field theory parameter fits to collider data beyond traditional rectangular cut-based differential distribution analyses. In this study, we focus on a SMEFT analysis of $pp\to t\bar{t}$ production, including top decays, where the linear effective field deformation is parametrised by thirteen independent Wilson coefficients. The application of GNNs allows us to condense the multidimensional phase space information available for the discrimination of BSM effects from the SM expectation by considering all available final state correlations directly. The number of contributing new physics couplings very quickly leads to statistical limitations when the GNN output is directly employed as an EFT discrimination tool. However, a selection based on minimising the SM contribution enhances the fit's sensitivity when reflected as a (non-rectangular) selection on the inclusive data samples that are typically employed when looking for non-resonant deviations from the SM by means of differential distributions.

17 pages, 7 figures, 3 tables; v2 accepted by JHEP

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Cited by in corpus (9)

• SmeftFR v3 -- Feynman rules generator for the Standard Model Effective Field Theory
• HHH Whitepaper
• Constraints on the trilinear and quartic Higgs couplings from triple Higgs production at the LHC and beyond
• On the BSM reach of four top production at the LHC
• Hypergraphs in LHC Phenomenology -- The Next Frontier of IRC-Safe Feature Extraction
• Interpretable deep learning models for the inference and classification of LHC data
• Collider-Flavour Complementarity from the bottom to the top
• Foundations of automatic feature extraction at LHC--point clouds and graphs
• Top-philic Machine Learning