Fast supersymmetry phenomenology at the large hadron collider using machine learning techniques

DOI10.1016/j.cpc.2011.12.026zbMath1264.81315arXiv1106.4613OpenAlexW1976621662MaRDI QIDQ1948836

Yanyan Li

Publication date: 25 April 2013

Published in: Computer Physics Communications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1106.4613

zbMATH Keywords

large hadron collidersupersymmetry phenomenology

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Unified quantum theories (81V22) Supersymmetric field theories in quantum mechanics (81T60)

Related Items (2)

Exploring supersymmetry with machine learning ⋮ Supervised Deep Learning in High Energy Phenomenology: a Mini Review*

Uses Software

• HERWIG 5.1
• ISAJet
• Herwig++
• AcerDET

Cites Work

• Unnamed Item
• Bayesian learning for neural networks
• Complete statistical theory of learning
• Bayesian MLP neural networks for image analysis
• Support Vector Machines

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