Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection

TL;DR

This paper introduces a Graph Transformer with Mixture-of-Experts layers that enhances interpretability and accuracy in particle collision event classification, linking model decisions to physics features.

Contribution

It presents a novel model combining Graph Transformers and Mixture-of-Experts for interpretable high-energy physics data analysis, improving transparency without sacrificing performance.

Findings

Achieves competitive classification accuracy on simulated collider data.

Provides interpretable insights aligned with physics principles.

Demonstrates potential for trustworthy AI in particle physics.

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 interpretability, 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 interpretability into the architecture. By leveraging attention maps and expert specialization, the model offers insights into its internal decision-making, linking predictions to physics-informed features. We evaluate the model on simulated events from the ATLAS experiment, focusing on distinguishing rare Supersymmetric signal events from Standard Model background. Our results highlight that the model achieves competitive classification accuracy while providing interpretable outputs that align with known physics, demonstrating its potential as a robust and transparent tool for high-energy physics data analysis. This approach underscores the importance of explainability in machine learning methods applied to high energy physics, offering a path toward greater trust in AI-driven discoveries.