{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/jet-reconstruction-with-mamba-networks-in","title":"Jet Reconstruction with Mamba Networks in Collider Events","arxiv_id":"2506.18336","date":"2025-06-23","proceeding":null,"authors":["Jinmian Li","Peng Li","Bingwei Long","Rao Zhang"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2506.18336v1","url_pdf":"https://arxiv.org/pdf/2506.18336v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"jet-reconstruction-with-mamba-networks-in","repo_url":"https://github.com/scu-heplab/seg-any-sm-jet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}