Boosting Sensitivity to $HH\to b\bar{b} γγ$ with Graph Neural Networks and XGBoost

Abstract: In this paper, we explore the use of advanced ML techniques to enhance the sensitivity of double Higgs boson searches in the ( HH \to b\bar{b}\gamma\gamma ) decay channel at $\sqrt{s} = $ 13.6 TeV. Two ML models are implemented and compared: a tree-based classifier using XGBoost, and a geometrical-based graph neural network classifier (GNN). We show that the geometrical model outperform 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 upper limit and Higgs boson self-coupling ($\kappa_{\lambda}$) constraints.