Graduate Program Events |
The second part focuses on the reconstruction of charged-particle tracks in the ATLAS Inner Tracker (ITk), which is confronted with the extreme pile-up conditions expected in the High-Luminosity phase of the Large Hadron Collider (HL-LHC). Given the anticipated increase in instantaneous luminosity and associated event complexity—resulting in up to 200 simultaneous interactions per bunch crossing—traditional reconstruction algorithms face significant computational challenges. To address this, a novel track reconstruction algorithm based on Graph Neural Networks (GNNs) has been developed and evaluated. Using full detector simulation data on realistic ITk geometry, we demonstrate competitive physics performance of the GNN-based tracking approach with respect to the current tracking algorithm. The computational efficiency is optimized and measured in detail. This approach shows significant potential for efficient pattern recognition in dense detector environments, leveraging modern hardware accelerators such as GPUs and FPGAs for fast and scalable event reconstruction.