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 → t 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.

Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

“…Permutation invariant set-based architectures (with no explicit graph structure) have been developed to address the combinatorics of jet matching in fully-hadronic t t events with novel attention mechanisms [61,62]. Graphs have also been used to represent decay chains in semi-leptonic t t decays, wherein particles are embedded as heterogeneous nodes and parent-child decay relationships are represented as edges [63].…”