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AI researchers compare explainability methods for jet tagging in particle physics

By PulseAugur Editorial · [2 sources] ·

Researchers have developed and compared three explainable AI (XAI) methods—GNNExplainer, GNNShap, and GradCAM—to understand the predictions of graph neural networks used in jet tagging at the Large Hadron Collider. The study adapted these XAI techniques to the Lund plane representation, which maps parton splittings to graph nodes. By introducing a physics-informed evaluation framework, the research quantifies how explanation quality varies across different energy regimes and assesses the correlation between AI-assigned importance and established jet substructure observables. AI

IMPACT Provides methods to interpret complex AI models in high-energy physics, potentially improving understanding of learned features.

RANK_REASON Academic paper presenting a comparative study of explainability methods for graph neural networks in a specific physics application.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI researchers compare explainability methods for jet tagging in particle physics

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Pahal D. Patel, Sanmay Ganguly ·

    Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane

    arXiv:2604.25885v1 Announce Type: cross Abstract: Graph neural networks such as ParticleNet and transformer based networks on point clouds such as ParticleTransformer achieve state-of-the-art performance on jet tagging benchmarks at the Large Hadron Collider, yet the physical rea…

  2. arXiv cs.LG TIER_1 English(EN) · Sanmay Ganguly ·

    Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane

    Graph neural networks such as ParticleNet and transformer based networks on point clouds such as ParticleTransformer achieve state-of-the-art performance on jet tagging benchmarks at the Large Hadron Collider, yet the physical reasoning behind their predictions remains opaque. We…

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