Work

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

Year: 2025

Type: article

Abstract: 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 \rightarrow 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 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 ($\kappa _{\lambda }$) constraints.

Source: Progress of Theoretical and Experimental Physics

DOI: 10.1093/ptep/ptaf157

Authors: Mohamed Belfkir, Mohamed Amin Loualidi, Salah Nasri

Institution: United Arab Emirates University

Language: English

Topic: Particle physics theoretical and experimental studies

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $979

Funder: Sponsoring Consortium for Open Access Publishing in Particle Physics