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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Accesso online: | https://arxiv.org/abs/2312.12272 |
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| _version_ | 1866916471454564352 |
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| author | Van Stroud, Samuel Pond, Nikita Hart, Max Barr, Jackson Rettie, Sébastien Facini, Gabriel Scanlon, Tim |
| author_facet | Van Stroud, Samuel Pond, Nikita Hart, Max Barr, Jackson Rettie, Sébastien Facini, Gabriel Scanlon, Tim |
| contents | In high-energy particle collisions, the reconstruction of secondary vertices from heavy-flavour hadron decays is crucial for identifying and studying jets initiated by $b$- or $c$-quarks. Traditional methods, while effective, require extensive manual optimisation and struggle to perform consistently across wide regions of phase space. Meanwhile, recent advancements in machine learning have improved performance but are unable to fully reconstruct multiple vertices. In this work we propose a novel approach to secondary vertex reconstruction based on recent advancements in object detection and computer vision. Our method directly predicts the presence and properties of an arbitrary number of vertices in a single model. This approach overcomes the limitations of existing techniques. Applied to simulated proton-proton collision events, our approach demonstrates significant improvements in vertex finding efficiency, achieving a 10% improvement over an existing state-of-the-art method. Moreover, it enables vertex fitting, providing accurate estimates of key vertex properties such as transverse momentum, radial flight distance, and angular displacement from the jet axis. When integrated into a flavour tagging pipeline, our method yields a 50% improvement in light-jet rejection and a 15% improvement in $c$-jet rejection at a $b$-jet selection efficiency of 70%. These results demonstrate the potential of adapting advanced object detection techniques for particle physics, and pave the way for more powerful and flexible reconstruction tools in high-energy physics experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_12272 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Secondary Vertex Reconstruction with MaskFormers Van Stroud, Samuel Pond, Nikita Hart, Max Barr, Jackson Rettie, Sébastien Facini, Gabriel Scanlon, Tim High Energy Physics - Experiment High Energy Physics - Phenomenology In high-energy particle collisions, the reconstruction of secondary vertices from heavy-flavour hadron decays is crucial for identifying and studying jets initiated by $b$- or $c$-quarks. Traditional methods, while effective, require extensive manual optimisation and struggle to perform consistently across wide regions of phase space. Meanwhile, recent advancements in machine learning have improved performance but are unable to fully reconstruct multiple vertices. In this work we propose a novel approach to secondary vertex reconstruction based on recent advancements in object detection and computer vision. Our method directly predicts the presence and properties of an arbitrary number of vertices in a single model. This approach overcomes the limitations of existing techniques. Applied to simulated proton-proton collision events, our approach demonstrates significant improvements in vertex finding efficiency, achieving a 10% improvement over an existing state-of-the-art method. Moreover, it enables vertex fitting, providing accurate estimates of key vertex properties such as transverse momentum, radial flight distance, and angular displacement from the jet axis. When integrated into a flavour tagging pipeline, our method yields a 50% improvement in light-jet rejection and a 15% improvement in $c$-jet rejection at a $b$-jet selection efficiency of 70%. These results demonstrate the potential of adapting advanced object detection techniques for particle physics, and pave the way for more powerful and flexible reconstruction tools in high-energy physics experiments. |
| title | Secondary Vertex Reconstruction with MaskFormers |
| topic | High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2312.12272 |