IAFormer uses boost-invariant pairwise quantities and differential attention to create a sparse Transformer that achieves state-of-the-art classification on top-quark and quark-gluon jet datasets while using over an order of magnitude fewer parameters than prior Particle Transformer models.
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An edge-weighted multi-graph GNN (E-PCN/KIGNet) that encodes four jet kinematic variables improves JetClass jet-tagging accuracy and attributes most predictions to angular separation and transverse momentum.
The document reports the first year of activity of the VBSCan COST Action network on vector-boson scattering phenomenology and experiments from a 2018 workshop.
citing papers explorer
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IAFormer: Interaction-Aware Transformer network for collider data analysis
IAFormer uses boost-invariant pairwise quantities and differential attention to create a sparse Transformer that achieves state-of-the-art classification on top-quark and quark-gluon jet datasets while using over an order of magnitude fewer parameters than prior Particle Transformer models.
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KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging
An edge-weighted multi-graph GNN (E-PCN/KIGNet) that encodes four jet kinematic variables improves JetClass jet-tagging accuracy and attributes most predictions to angular separation and transverse momentum.
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VBSCan Thessaloniki 2018 Workshop Summary
The document reports the first year of activity of the VBSCan COST Action network on vector-boson scattering phenomenology and experiments from a 2018 workshop.
- Looking inside jets: an introduction to jet substructure and boosted-object phenomenology