Year: 2025
Type: article
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.
DOI: 10.1103/pwp8-ffqk
Authors: Jinmian Li, Peng Li, Bingwei Long, Rao Zhang
Institutions: Sichuan University, Institute of Modern Physics
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414630962