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

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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• arXiv
• Guillaume Letellier
• JetClass
• JetParticle-JEPA
• JP-JEPA
• Large Hadron Collider
• Particle Transformer
• Quark-Gluon Tagging
• top quark

• paper
• other

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