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Found 3 papers, 1 papers with code
Data Science and Machine Learning in Education
The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research.
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics
Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering.
Lorentz Boost Networks: Autonomous Physics-Inspired Feature Engineering
The LBN also enables the formation of further composite particles, which are then transformed into said rest frames by Lorentz transformation.
High Energy Physics - Experiment High Energy Physics - Phenomenology
