ATLAS ITk Track Reconstruction with a GNN-based pipeline

In preparation for the upcoming HL-LHC era, ATLAS is pursuing several methods to reduce the resources consumption needed to reconstruct the trajectory of charged particles (tracks) in the new all-silicon Inner Tracker (ITk). This includes the development of new algorithms suitable for massively parallel computing architecture like GPUs. Algorithms for track pattern recognition based on graph neural networks (GNNs) have emerged as a particularly promising approach. Previous work using simulated data from the TrackML challenge show high track reconstruction efficiency. In the present document we describe a first functional implementation of a GNN-based track pattern reconstruction for ITk, achieving a high GNN track reconstruction efficiency and promising fake track rate.