Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2401.14198 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866929225976512512 |
|---|---|
| author | Chiang, Cheng-Wei Hsieh, Feng-Yang Hsu, Shih-Chieh Low, Ian |
| author_facet | Chiang, Cheng-Wei Hsieh, Feng-Yang Hsu, Shih-Chieh Low, Ian |
| contents | The study of di-Higgs events, both resonant and non-resonant, plays a crucial role in understanding the fundamental interactions of the Higgs boson. In this work we consider di-Higgs events decaying into four $b$-quarks and propose to improve the experimental sensitivity by utilizing a novel machine learning algorithm known as Symmetry Preserving Attention Network (\textsc{Spa-Net}) -- a neural network structure whose architecture is designed to incorporate the inherent symmetries in particle reconstruction tasks. We demonstrate that the \textsc{Spa-Net} can enhance the experimental reach over baseline methods such as the cut-based and the Deep Neural Networks (DNN)-based analyses. At the Large Hadron Collider, with a 14-TeV centre-of-mass energy and an integrated luminosity of 300 fb$^{-1}$, the \textsc{Spa-Net} allows us to establish 95\% C.L. upper limits in resonant production cross-sections that are 10\% to 45\% stronger than baseline methods. For non-resonant di-Higgs production, \textsc{Spa-Net} enables us to constrain the self-coupling that is 9\% more stringent than the baseline method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14198 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning to Improve the Sensitivity of Di-Higgs Searches in the $4b$ Channel Chiang, Cheng-Wei Hsieh, Feng-Yang Hsu, Shih-Chieh Low, Ian High Energy Physics - Phenomenology High Energy Physics - Experiment The study of di-Higgs events, both resonant and non-resonant, plays a crucial role in understanding the fundamental interactions of the Higgs boson. In this work we consider di-Higgs events decaying into four $b$-quarks and propose to improve the experimental sensitivity by utilizing a novel machine learning algorithm known as Symmetry Preserving Attention Network (\textsc{Spa-Net}) -- a neural network structure whose architecture is designed to incorporate the inherent symmetries in particle reconstruction tasks. We demonstrate that the \textsc{Spa-Net} can enhance the experimental reach over baseline methods such as the cut-based and the Deep Neural Networks (DNN)-based analyses. At the Large Hadron Collider, with a 14-TeV centre-of-mass energy and an integrated luminosity of 300 fb$^{-1}$, the \textsc{Spa-Net} allows us to establish 95\% C.L. upper limits in resonant production cross-sections that are 10\% to 45\% stronger than baseline methods. For non-resonant di-Higgs production, \textsc{Spa-Net} enables us to constrain the self-coupling that is 9\% more stringent than the baseline method. |
| title | Deep Learning to Improve the Sensitivity of Di-Higgs Searches in the $4b$ Channel |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2401.14198 |