Double Metric Learning for Building Directed Graphs with Chain Connections for the ATLAS ITk Detector
This paper proposes a "Double Metric Learning" approach that learns two distinct node representations to resolve conflicts in constructing directed graphs with chain connections for the ATLAS ITk detector, demonstrating improved performance in graph construction and edge direction prediction for high transverse momentum particles compared to simple metric learning.