The present problem with High Luminosity Large Hadron Collider experiment is reconstructing particle trajectories from massive event data. When hadrons collide, the resulting particles form a point cloud, but only charged particles are relevant for further analysis and First Level Event Selection (FLES). In current detectors, sensors can only register whether a particle passed through them, making trajectory reconstruction difficult. Traditional Combinatorial Kalman Filters (CKF) try many possible track combinations, which causes a combinatorial explosion. Graph Neural Networks have also been applied, but their edge-heavy computation reduces speed. Vision Transformer (ViT)-based methods improved the task by treating it as a computer vision problem, but they still scale quadratically. We propose a new heuristic-based method that reconstructs trajectories in linear time, O(N). Our algorithm is about 23% faster than ViT and 140% faster than CKF, while maintaining strong trajectory estimation performance, providing a much faster and simpler alternative for realtime particle track reconstruction in HL-LHC experiments.
Gazi et al. (Fri,) studied this question.