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KIGNet: Physics-motivated graph learning for explainable jet tagging

By PulseAugur Editorial · [1 sources] ·

Researchers have developed KIGNet, a new graph neural network designed for explainable jet tagging in high-energy physics. KIGNet integrates kinematic variables like angular separation, relative transverse momentum, momentum fraction, and invariant mass squared into its classification process. The model demonstrates that it learns physically interpretable representations, with angular separation and relative transverse momentum being the most dominant factors in classification, aligning with theoretical predictions of QCD radiation. KIGNet achieves state-of-the-art performance on benchmark datasets, showing significant improvements in accuracy and representation quality compared to existing methods. AI

IMPACT Enhances explainability in physics-based AI models, potentially improving scientific discovery and model trust.

RANK_REASON The cluster contains a research paper detailing a new model and its performance on scientific benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

KIGNet: Physics-motivated graph learning for explainable jet tagging

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The cluster contains a research paper detailing a new model and its performance on scientific benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Md Raqibul Islam, Adrita Khan, Mir Sazzat Hossain, Choudhury Ben Yamin Siddiqui, Md. Zakir Hossan, Tanjib Khan, M. Arshad Momen, Amin Ahsan Ali, AKM Mahbubur Rahman ·

    KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging

    arXiv:2512.07420v3 Announce Type: replace-cross Abstract: Jet identification plays a central role in analyzing data from high-energy collider experiments. While deep learning has improved jet classification, it often lacks interpretability. We introduce the Kinematic Interaction …

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