Boosting Sensitivity to HH → bb̄γγ with Graph Neural Networks and XGBoost

In this paper, we explore the use of advanced machine learning (ML) techniques to enhance the sensitivity of double Higgs boson searches in the HH → bbγγ decay channel at √s = 13.6 TeV. Two ML models are implemented and compared: a tree-based classifier using XGBoost, and a geometry-based graph neural network classifier (GNN). We show that the geometrical model outperforms the traditional XGBoost classifier, improving the expected 95% CL upper limit on the double Higgs boson production cross section by 28%. Our results are compared to the latest ATLAS experiment results, showing significant improvement of both the upper limit and Higgs boson self-coupling (κ_λ) constraints.