Boosting Sensitivity to with Graph Neural Networks and XGBoost
This paper demonstrates that a Graph Neural Network (GNN) outperforms an XGBoost classifier in enhancing the sensitivity of searches at 13.6 TeV, significantly improving the expected upper limits on the double Higgs production cross-section and the Higgs boson self-coupling () compared to current ATLAS results.