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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4 Pith papers cite this work. Polarity classification is still indexing.
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E-PCN reaches 94.67% macro-accuracy on 10-class jet tagging by weighting graphs with angular separation, transverse momentum, momentum fraction, and invariant mass, with Grad-CAM showing the first two account for 76% of decisions and yielding gains over baseline PCN.
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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E-PCN: Jet Tagging with Explainable Particle Chebyshev Networks Using Kinematic Features
E-PCN reaches 94.67% macro-accuracy on 10-class jet tagging by weighting graphs with angular separation, transverse momentum, momentum fraction, and invariant mass, with Grad-CAM showing the first two account for 76% of decisions and yielding gains over baseline PCN.
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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