Probing SMEFT Operators through tbarttbart Production with Hyper-Graph Neural Networks at the LHC

We present a phenomenological study of tbarttbart production in proton-proton collisions at √s = 13~TeV, using a Hyper-Graph Neural Network (H-GNN) to discriminate multilepton signal events from the dominant SM backgrounds, namely tbartW, tbartZ, tbartH, tbartVV, single-top associated production, and diboson and triboson processes. In the H-GNN architecture each event is represented as a hypergraph whose nodes correspond to reconstructed jets and leptons and whose hyperedges encode higher-order correlations among arbitrary subsets of these objects, allowing the network to learn the many-body kinematic structures that characterize the tbarttbart final state. Combining same-sign di-lepton, tri-lepton, and four-lepton channels following a CMS-like event selection, the H-GNN attains an area under the ROC curve of 0.951 for the tbarttbart signal and yields a statistical significance of Z = 9.11 at an integrated luminosity of L = 140~fb-1, to be compared with Z = 8.62 for a SPANet baseline, Z = 7.37 for a Particle Transformer baseline, and Z = 5.13 obtained by the ATLAS analysis, evaluated under identical event selection. We exploit the improved signal extraction to derive one- and two-parameter 95% confidence level limits on the Wilson coefficients of the dimension-six operators OPhi u, O(1)tt, O(1)qq, O(1)qt, and O(8)qt, and we project the expected sensitivity at the HL-LHC integrated luminosities of 1000~fb-1 and 3000~fb-1 with 50% uncertainty on the background estimation.