HiGTR: High-Performance FPGA Implementation of Complete GNN-based Trajectory Reconstruction for HEP

Charged particle trajectory reconstruction is a critical task in high-energy physics (HEP), particularly for collision analysis in the Large Hadron Collider (LHC). In the LHC, the Level-1 Trigger (L1T) system must perform trajectory reconstruction with ultralow latency and very high throughput. Graph Neural Networks (GNNs)-based trajectory reconstruction on FPGAs has shown promising performance. However, the existing FPGA-based implementations are incomplete, supporting only the GNN processing stage and lacking the graph construction and track building parts of the task flow.