Speaker
Description
The High-Luminosity LHC (HL-LHC) will deliver proton-proton collisions at unprecedented pile-up conditions, making fast and efficient track reconstruction a key computational challenge. Graph Neural Networks (GNNs) offer a promising approach to this problem.
A critical first step in GNN-based charged-particle tracking is graph construction, which can be carried out with the Metric Learning method. In this approach, a Multi-Layer Perceptron embeds hits into a latent space, where successive hits from the same particle are clustered and unrelated hits are separated; edges are then formed between hits that lie close together in this embedding.
This contribution presents the Metric Learning stage of a GNN-based tracking pipeline developed for the ATLAS Event Filter, the software-based trigger responsible for the final online event selection at the HL-LHC. Particular emphasis is placed on strategies for hardware-constrained deployment. Regional training, in which models are trained on geometrically partitioned subsets of the ATLAS Inner Tracker to reduce their size and complexity, is investigated. In addition, quantization and compression techniques, originally developed for FPGA implementation within the ATLAS Event Filter, are evaluated. The method’s performance is presented, together with an assessment of these optimization strategies for real-time operation. The optimization strategies discussed are of general relevance to trigger and data acquisition systems operating under similar latency and resource constraints, with the potential for implementation on GPUs as well as FPGAs.