Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection (2501.03432v2)

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.

• The paper introduces a novel model architecture combining Graph Transformers with Mixture-of-Experts layers for enhanced collision detection.
• It employs multi-head self-attention and expert gating to deliver high classification performance while providing transparent, interpretable insights.
• Empirical results on ATLAS simulated data demonstrate competitive accuracy in distinguishing SUSY signals from Standard Model backgrounds.

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

The paper "Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection" presents a novel approach integrating Graph Transformers with Mixture-of-Expert (MoE) layers, targeting the challenging task of particle collision detection at CERN's Large Hadron Collider (LHC). The research emphasizes marrying high predictive accuracy with model interpretability—a key requirement in high-energy physics where understanding the reasoning behind predictions is as crucial as the predictions themselves.

At the core of this paper is the use of graph-based representations for particle collisions, leveraging Graph Neural Networks (GNNs) which are adept at capturing complex relationships inherent in graph-structured data. The innovation here lies in the combination of a Transformer architecture with MoE layers. This configuration not only retains the state-of-the-art classification capabilities of GNNs but also embeds transparency directly into the model's structure.

Key Contributions

1. Model Architecture:
   • The paper introduces a Graph Transformer enhanced with MoE layers. The Transformer utilizes multi-head self-attention mechanisms to process graph-structured data, where each node represents a particle. The attention mechanism provides insights into the model's focus, potentially linking graph structural regions with known physical phenomena.
   • The MoE layer, which replaces the traditional feed-forward layer in the Transformer, contributes to interpretability by ensuring that subsets of the model (experts) specialize in distinct aspects of the data. The architectural design includes a gating mechanism that dynamically assigns inputs to the most relevant expert networks.
2. Interpretability:
   • By embedding interpretability into the architecture, the model can visualize attention maps that highlight important graph regions, helping to verify alignment with known physics principles. Additionally, expert specialization elucidates the internal decision-making processes, marking a departure from conventional "black box" machine learning models.
3. Empirical Results:
   • The model was evaluated using simulated data from the ATLAS experiment, tasked with differentiating supersymmetric (SUSY) signal events from Standard Model (SM) backgrounds. The results indicate that this model retains competitive classification performance while offering outputs that are more interpretable compared to traditional methods. This balance underscores the potential of the model as a reliable tool in data analysis within the domain of high-energy physics.

Implications and Future Directions

The dual emphasis on performance and explainability is strategically significant for fields that require transparency in algorithmic decision-making, such as particle physics. This work not only enhances current methodological frameworks but also lays groundwork for future AI-driven discoveries where trust in machine learning models is paramount. The intrinsic explainability opens pathways for further refinement of AI models in physics, potentially leading to greater integration of AI into experimental workflows without compromising on the scientific rigor traditionally upheld in the field.

In anticipation of future developments, expanding this methodology to accommodate larger datasets or more complex graph structures could be beneficial. Research might also focus on generalizing this hybrid model to other high-energy physics tasks, thereby solidifying its applicability across various scenarios. Furthermore, the integration of automated explainability tools and more comprehensive interpretability techniques could augment the utility and acceptance of such models in this domain.

In summary, this paper provides a well-rounded advancement in the application of machine learning to particle physics, successfully intertwining strong predictive accuracy with much-needed interpretability, fostering a path toward more transparent and trustworthy AI systems in scientific research.