Speakers
Description
In recent years, Graph Neural Networks (GNNs) have emerged as a transformative tool in particle physics, offering a powerful framework for analyzing complex, non-Euclidean data structures such as particle interactions and detector outputs. This session will provide an exploration of GNNs, bridging their theoretical foundations with practical applications in particle physics. We will highlight their relevance to cutting-edge research at CERN (e.g., ATLAS and CMS), where GNNs are revolutionizing tasks such as particle identification, event classification, and detector signal reconstruction. The hands-on section will feature two critical tasks that showcase the importance of GNNs: a supervised task (particle identification) and a weakly supervised task (particle shower reconstruction). We will use Python-based frameworks and tools (i.e., PyTorch) widely used for GNN development and deployment.
