Mixture-of-experts graph transformers for interpretable particle collision detection.

ABSTRACT: The Large Hadron Collider at CERN produces immense volumes of complex data from high-energy particle collisions, demanding sophisticated analytical techniques for effective interpretation. Neural Networks, including Graph Neural Networks, have shown promise in tasks such as event classification and object identification by representing collisions as graphs. However, while Graph Neural Networks excel in predictive accuracy, their "black box" nature often limits their explainability, making it difficult to trust their decision-making processes. In this paper, we propose a novel approach that combines a Graph Transformer model with Mixture-of-Expert layers to achieve high predictive performance while embedding explainability into the architecture. By leveraging attention maps and expert speci

SUBMITTER: Genovese D 

PROVIDER: S-EPMC12314041 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature