Jet Reconstruction with Mamba Networks in Collider Events
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914055320502272 |
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| author | Li, Jinmian Li, Peng Long, Bingwei Zhang, Rao |
| author_facet | Li, Jinmian Li, Peng Long, Bingwei Zhang, Rao |
| contents | We introduce a novel end-to-end framework for jet reconstruction in high-energy collider events, leveraging the efficiency and long-range modeling capabilities of the Mamba architecture. Our model unifies instance segmentation, classification, and kinematic regression into a single multi-task learning system, enabling a sophisticated multi-level reconstruction that simultaneously identifies primary heavy jets ($t$, $H$, $W/Z$) and their constituent sub-jets. To facilitate supervised learning for this complex task, we develop a novel method for assigning final-state hadrons to their ancestor colored partons using a Mixed-Integer Linear Programming solver, which generates high-fidelity ground-truth labels. The model achieves high classification accuracy, with an Average Precision score of 0.569 for $W/Z$-jets and 0.568 for $b$-jets, and shows exceptional precision in kinematic reconstruction. Furthermore, we show that the model not only maintains stable performance in high-pileup environments but also successfully reconstructs the mass peaks of beyond the standard model particles. This work presents a powerful and versatile new tool for comprehensive event reconstruction at the LHC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18336 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Jet Reconstruction with Mamba Networks in Collider Events Li, Jinmian Li, Peng Long, Bingwei Zhang, Rao High Energy Physics - Phenomenology High Energy Physics - Experiment We introduce a novel end-to-end framework for jet reconstruction in high-energy collider events, leveraging the efficiency and long-range modeling capabilities of the Mamba architecture. Our model unifies instance segmentation, classification, and kinematic regression into a single multi-task learning system, enabling a sophisticated multi-level reconstruction that simultaneously identifies primary heavy jets ($t$, $H$, $W/Z$) and their constituent sub-jets. To facilitate supervised learning for this complex task, we develop a novel method for assigning final-state hadrons to their ancestor colored partons using a Mixed-Integer Linear Programming solver, which generates high-fidelity ground-truth labels. The model achieves high classification accuracy, with an Average Precision score of 0.569 for $W/Z$-jets and 0.568 for $b$-jets, and shows exceptional precision in kinematic reconstruction. Furthermore, we show that the model not only maintains stable performance in high-pileup environments but also successfully reconstructs the mass peaks of beyond the standard model particles. This work presents a powerful and versatile new tool for comprehensive event reconstruction at the LHC. |
| title | Jet Reconstruction with Mamba Networks in Collider Events |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2506.18336 |