Secondary vertex reconstruction with MaskFormers
Abstract 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 consist...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-10-01
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| Series: | European Physical Journal C: Particles and Fields |
| Online Access: | https://doi.org/10.1140/epjc/s10052-024-13374-5 |
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| _version_ | 1849220578795323392 |
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| author | Samuel Van Stroud Nikita Pond Max Hart Jackson Barr Sébastien Rettie Gabriel Facini Timothy Scanlon |
| author_facet | Samuel Van Stroud Nikita Pond Max Hart Jackson Barr Sébastien Rettie Gabriel Facini Timothy Scanlon |
| author_sort | Samuel Van Stroud |
| collection | DOAJ |
| description | Abstract 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 | Article |
| id | doaj-art-810b888a008d441eada536a6477775ae |
| institution | Kabale University |
| issn | 1434-6052 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Physical Journal C: Particles and Fields |
| spelling | doaj-art-810b888a008d441eada536a6477775ae2024-12-08T12:43:19ZengSpringerOpenEuropean Physical Journal C: Particles and Fields1434-60522024-10-01841011010.1140/epjc/s10052-024-13374-5Secondary vertex reconstruction with MaskFormersSamuel Van Stroud0Nikita Pond1Max Hart2Jackson Barr3Sébastien Rettie4Gabriel Facini5Timothy Scanlon6Centre for Data Intensive Science and Industry, University College LondonCentre for Data Intensive Science and Industry, University College LondonCentre for Data Intensive Science and Industry, University College LondonCentre for Data Intensive Science and Industry, University College LondonCentre for Data Intensive Science and Industry, University College LondonCentre for Data Intensive Science and Industry, University College LondonCentre for Data Intensive Science and Industry, University College LondonAbstract 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.https://doi.org/10.1140/epjc/s10052-024-13374-5 |
| spellingShingle | Samuel Van Stroud Nikita Pond Max Hart Jackson Barr Sébastien Rettie Gabriel Facini Timothy Scanlon Secondary vertex reconstruction with MaskFormers European Physical Journal C: Particles and Fields |
| title | Secondary vertex reconstruction with MaskFormers |
| title_full | Secondary vertex reconstruction with MaskFormers |
| title_fullStr | Secondary vertex reconstruction with MaskFormers |
| title_full_unstemmed | Secondary vertex reconstruction with MaskFormers |
| title_short | Secondary vertex reconstruction with MaskFormers |
| title_sort | secondary vertex reconstruction with maskformers |
| url | https://doi.org/10.1140/epjc/s10052-024-13374-5 |
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