Explaining machine-learned particle-flow reconstruction

The particle-flow (PF) algorithm is used in general-purpose particle detectors to reconstruct a comprehensive particle-level view of the collision by combining information from different subdetectors. A graph neural network model, known as the MLPF algorithm, has been developed to substitute rule-based PF. However, understanding the model's decision making is not straightforward, especially given the complexity of the set-to-set prediction task, dynamic graph building, and message-passing steps. In this paper, we adapt the layerwise-relevance propagation technique to the MLPF algorithm to gauge the relevant nodes and features for its predictions. Through this we gain insight into the model's decision-making.