Speaker
Description
Graph construction is an essential step in the Graph Neural Network (GNN) based tracking pipelines. The goal of the graph construction is to construct a graph that contains only true edge connections between nodes (detector spacepoints). A promising approach for the graph construction is through the metric learning, where a node embedding space is learned, and nodes are connected according to their distance in the embedding space. The loss function for the metric learning in this case is a contrastive loss encouraging the true pairs of nodes to be close to each other, and pulling away the false pairs of nodes. This loss function presents a conflict for the hopping connections when the true connection is defined as the chain connection in a particle track. To address the conflict for this case, we propose to learn two node embedding spaces. A directed graph can then be constructed based on the distance between a source node in the first embedding space and a target node in the second embedding space. We test this idea with the ATLAS ITk detector at the HL-LHC using the ATLAS ITk simulation and show better graph construction efficiency and purity compared to the single metric learning graph construction.
Once the graphs are constructed, one can learn a GNN to either classify edges into true and fake edges (edge classification), or learn a node embedding space for node clustering (object condensation). A common problem with these approaches is that it does not handle the situation where a spacepoint is shared by multiple particle tracks. In this presentation, we propose a GNN model that learns an embedding space for the spacepoint doublet. A clustering can then be performed in the spacepoint doublet embedding space to extract track candidates. Alternatively, combining with the edge classification approach, the spacepoint doublet embedding can be used to resolve connected components formed by multiple particle tracks sharing the same spacepoints. We take the ATLAS ITk detector at the HL-LHC as a realistic example and show promising tracking performance with the ATLAS ITk simulation. We also show that we are able to assign shared spacepoints to multiple track candidates with the learning of edge embedding space.