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→bbˉγγ decay channel at 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 (κλ) constraints.
@article{arxiv.2508.01449,
title = {Boosting Sensitivity to $HH\to b\bar{b} \gamma\gamma$ with Graph Neural Networks and XGBoost},
author = {Mohamed Belfkir and Mohamed Amin Loualidi and Salah Nasri},
journal= {arXiv preprint arXiv:2508.01449},
year = {2026}
}