Fast and Precise Learned Charged-Particle Trajectory Regression at the Large Hadron Collider
This study addresses the prohibitive computational cost of traditional algorithms for charged particle track fitting at the Large Hadron Collider (LHC), which hinders large-scale data processing. We formulate track parameter regression as a sequential task and propose an end-to-end learning approach based on a Bidirectional Gated Linear Recurrent encoder (BiGLR) to replace conventional Kalman filtering and least-squares fitting. This work is the first to achieve end-to-end track fitting with full classical precision, further accelerated by custom GPU kernels during inference. Experiments demonstrate that the proposed method reproduces the $10^{-5}$-level accuracy of traditional algorithms while substantially reducing computational costs under equivalent hardware conditions. Consequently, this approach establishes an efficient new paradigm for pattern recognition in LHC experiments.