QCD
PulseAugur coverage of QCD — every cluster mentioning QCD across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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New Combinatorial Residual Network Classifier Developed for Particle Physics
Researchers have developed a new classifier called the Combinatorial Residual Network (CoReNet) designed to handle complex combinatorial problems in particle physics, specifically for identifying the decay chains of hea…
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David Gross group reviews new QCD existence proof
A group led by David Gross is reviewing a new physics concept that may provide an existence proof for QCD. The work is not yet released, and its practical applications or computational requirements are not specified. Th…
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New RINO method uses renormalization group for self-supervised learning in physics
Researchers have introduced RINO, a novel self-supervised learning method for high-energy physics that leverages the multi-scale structure of jet formation. Unlike previous methods, RINO uses renormalization group princ…
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Machine learning advances integrated into high-energy nuclear physics research
A new review paper explores the integration of machine learning (ML) into high-energy nuclear physics (HENP). The paper highlights recent advancements where physical knowledge is increasingly incorporated into data anal…
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Conformal deep learning models pion form factor from first principles
Researchers have developed a Physics-Informed Neural Network (PINN) utilizing a conformal z-plane to extract the pion electromagnetic form factor $F_{\pi}(s)$. This novel approach integrates fundamental S-matrix princip…
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New PLuM Architecture Enhances Jet Taggers with Multimodal Physics Data
Researchers have developed a new multimodal architecture called PLuM that combines particle constituents with Lund plane splittings for improved jet tagging in high-energy physics. This approach processes both types of …
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AI researchers compare explainability methods for jet tagging in particle physics
Researchers have developed and compared three explainable AI (XAI) methods—GNNExplainer, GNNShap, and GradCAM—to understand the predictions of graph neural networks used in jet tagging at the Large Hadron Collider. The …