Brief

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

Researchers have developed PhyGHT, a novel Physics-Guided Hypergraph Transformer, to improve signal purification for the High-Luminosity Large Hadron Collider (HL-LHC). This architecture combines graph attention with global self-attention, incorporating a physics-constrained Pileup Suppression Gate to filter noise before data aggregation. The model demonstrates superior performance over existing methods in reconstructing top-quark pair production signals under extreme pileup conditions, enhancing the HL-LHC's discovery potential. AI