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New self-supervised method enhances jet tagging in high-energy physics

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Researchers have developed JetParticle-JEPA (JP-JEPA), a novel self-supervised learning method for jet tagging in high-energy physics. This approach, built on a Particle Transformer, learns meaningful representations directly from particle data without requiring extensive labeled datasets. JP-JEPA demonstrates performance comparable to supervised methods on benchmarks like JetClass, and shows improved robustness to detector mismodeling and data limitations. AI

RANK_REASON The cluster contains an academic paper detailing a new method for a specific scientific domain. [lever_c_demoted from research: ic=1 ai=0.7]

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New self-supervised method enhances jet tagging in high-energy physics

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The cluster contains an academic paper detailing a new method for a specific scientific domain. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Guillaume Letellier (LPCC), Antonin Vacheret (LPCC), Fr\'ed\'eric Jurie ·

    JetParticle-JEPA: An Efficient Self-Supervised Representation Learning method for Jet Tagging in High-Energy Physics

    arXiv:2606.14813v1 Announce Type: cross Abstract: Jet tagging at the Large Hadron Collider increasingly relies on deep learning models trained on massive simulated datasets, leading to high computational costs and limited robustness to detector mismodeling. We introduce JetPartic…

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