Boosting Sensitivity to $HH\to b\bar{b} γγ$ with Graph Neural Networks and XGBoost
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912892423503872 |
|---|---|
| author | Belfkir, Mohamed Loualidi, Mohamed Amin Nasri, Salah |
| author_facet | Belfkir, Mohamed Loualidi, Mohamed Amin Nasri, Salah |
| contents | In this paper, we explore the use of advanced machine learning (ML) techniques to enhance the sensitivity of double Higgs boson searches in the \( HH \to b\bar{b}γγ\) 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 ($κ_λ$) constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01449 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Boosting Sensitivity to $HH\to b\bar{b} γγ$ with Graph Neural Networks and XGBoost Belfkir, Mohamed Loualidi, Mohamed Amin Nasri, Salah High Energy Physics - Phenomenology High Energy Physics - Experiment In this paper, we explore the use of advanced machine learning (ML) techniques to enhance the sensitivity of double Higgs boson searches in the \( HH \to b\bar{b}γγ\) 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 ($κ_λ$) constraints. |
| title | Boosting Sensitivity to $HH\to b\bar{b} γγ$ with Graph Neural Networks and XGBoost |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2508.01449 |