Speaker
Description
Current state-of-the-art in charged particle tracking follows a two-step paradigm where a graph neural network optimizes an intermediate prediction-loss during training and is later combined with a discrete, non-differentiable, optimization step during inference, constructing disconnected track candidates. In this talk, we introduce and assess a novel end-to-end differentiable tracking strategy. We use edge-classifying graph neural networks with differentiable combinatorial components, enabling direct optimization of the task-loss for discrete assignments. We provide further insights into the optimization processes and learned solutions, demonstrating similarities and limitations of two-step and end-to-end optimization. Finally, we demonstrate through a proof of concept that our approach can encode additional constraints or objective functions of downstream tasks, enabling the optimization of tracking solutions that meet specific performance criteria.