Speaker
Description
Charged-particle track reconstruction is a central challenge for the High-Luminosity Large Hadron Collider (HL-LHC), where high detector occupancy and the strongly curved trajectories of low-$p_T$ particles create severe combinatorial ambiguities. Although Graph Neural Networks (GNNs) have emerged as a promising approach for graph-based tracking, their performance is often limited by the quality of the underlying graph representation. We propose Manifold Helical-IN, a physics-informed framework that incorporates detector geometry and charged-particle trajectory information throughout the reconstruction pipeline. Candidate hit pairs are constructed by connecting only detector layers compatible with charged-particle propagation; pseudorapidity and azimuthal constraints reject geometrically inconsistent combinations, while the search region is adaptively enlarged according to the expected track curvature to retain low-$p_T$ trajectories without substantially increasing combinatorial background. The selected detector hits are then projected onto a helical manifold representation that follows the expected motion of charged particles in the solenoidal magnetic field, enabling the interaction network to distinguish genuine track segments from spurious connections more effectively. During message passing, information exchanged between neighboring hits is further weighted according to their geometric consistency with the underlying helical trajectory. Evaluated on the TrackML challenge dataset, Manifold Helical-IN achieves a reconstruction efficiency of 0.9675 in the benchmark region of $p_T > 500$ MeV, while in the challenging $p_T$ range of 380-460 MeV efficiency was 0.8496. The fake-track rate achieved was 0.0037, corresponding to a 13-fold improvement over the baseline Helical-IN architecture in the fake rate, while maintaining an inference latency of approximately 150 ms per event on a single NVIDIA A100 GPU. These results demonstrate that explicitly embedding detector geometry and charged-particle trajectory constraints into graph neural networks can substantially improve track purity while preserving computational efficiency, highlighting the potential of geometry-aware graph learning for scalable charged-particle track reconstruction at the HL-LHC.