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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.15477 |
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| _version_ | 1866929321466134528 |
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| author | Lu, Zejia Chen, Xiang Wu, Jiahui Zhang, Yulei Li, Liang |
| author_facet | Lu, Zejia Chen, Xiang Wu, Jiahui Zhang, Yulei Li, Liang |
| contents | Beam dump experiments provide a distinctive opportunity to search for dark photons, which are compelling candidates for dark matter with low mass. In this study, we propose the application of Graph Neural Networks (GNN) in tracking reconstruction with beam dump experiments to obtain high resolution in both tracking and vertex reconstruction. Our findings demonstrate that in a typical 3-track scenario with the visible decay mode, the GNN approach significantly outperforms the traditional approach, improving the 3-track reconstruction efficiency by up to 88% in the low mass region. Furthermore, we show that improving the minimal vertex detection distance significantly impacts the signal sensitivity in dark photon searches with the visible decay mode. By reducing the minimal vertex distance from 5 mm to 0.1 mm, the exclusion upper limit on the dark photon mass ($m_A\prime$) can be improved by up to a factor of 3. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15477 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Application of Graph Neural Networks in Dark Photon Search with Visible Decays at Future Beam Dump Experiment Lu, Zejia Chen, Xiang Wu, Jiahui Zhang, Yulei Li, Liang High Energy Physics - Experiment Instrumentation and Detectors Beam dump experiments provide a distinctive opportunity to search for dark photons, which are compelling candidates for dark matter with low mass. In this study, we propose the application of Graph Neural Networks (GNN) in tracking reconstruction with beam dump experiments to obtain high resolution in both tracking and vertex reconstruction. Our findings demonstrate that in a typical 3-track scenario with the visible decay mode, the GNN approach significantly outperforms the traditional approach, improving the 3-track reconstruction efficiency by up to 88% in the low mass region. Furthermore, we show that improving the minimal vertex detection distance significantly impacts the signal sensitivity in dark photon searches with the visible decay mode. By reducing the minimal vertex distance from 5 mm to 0.1 mm, the exclusion upper limit on the dark photon mass ($m_A\prime$) can be improved by up to a factor of 3. |
| title | Application of Graph Neural Networks in Dark Photon Search with Visible Decays at Future Beam Dump Experiment |
| topic | High Energy Physics - Experiment Instrumentation and Detectors |
| url | https://arxiv.org/abs/2401.15477 |