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  • Open Access

Jet reconstruction with Mamba networks in collider events

Jinmian Li1,*, Peng Li1,†, Bingwei Long1,2,‡, and Rao Zhang1,§

  • *Contact author: jmli@scu.edu.cn
  • †Contact author: lipeng@scu.edu.cn
  • ‡Contact author: bingwei@scu.edu.cn
  • §Contact author: rzhang9527@gmail.com
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Phys. Rev. D 112, 056017 – Published 15 September, 2025

DOI: https://doi.org/10.1103/pwp8-ffqk

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 multitask learning system, enabling a sophisticated multilevel reconstruction that simultaneously identifies primary heavy jets (t, H, W/Z) and their constituent subjets. 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.

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Phys. Rev. D 112, 056017– Published 15 September, 2025
  • Received 30 June 2025
  • Accepted 25 August 2025
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