JetClass
PulseAugur coverage of JetClass — every cluster mentioning JetClass across labs, papers, and developer communities, ranked by signal.
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KIGNet: Physics-motivated graph learning for explainable jet tagging
Researchers have developed KIGNet, a new graph neural network designed for explainable jet tagging in high-energy physics. KIGNet integrates kinematic variables like angular separation, relative transverse momentum, mom…
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New Study Compares Pre-Training Methods for AI in High-Energy Physics
A new arXiv paper explores pre-training objectives for foundation models in simulation-based sciences, specifically focusing on high-energy physics. The study compares supervised classification, flow-matching generation…
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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 di…
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Quantum-Inspired Methods Boost Machine Learning Representations
Researchers have developed new methods to enhance machine learning models by integrating quantum computing principles. One approach, QUIVER, uses quantum Fisher views to capture higher-order correlations in data, improv…