Neural network generated parametrizations of deeply virtual Compton form factors

DOI10.1007/JHEP07(2011)073zbMath1298.81397arXiv1106.2808MaRDI QIDQ467265

Krešimir Kumerički, Dieter Müller, Andreas Schäfer

Publication date: 3 November 2014

Published in: Journal of High Energy Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1106.2808

zbMATH Keywords

QCD phenomenology

Mathematics Subject Classification ID

Strong interaction, including quantum chromodynamics (81V05) Electromagnetic interaction; quantum electrodynamics (81V10) Neural nets applied to problems in time-dependent statistical mechanics (82C32) (2)-body potential quantum scattering theory (81U05)

Uses Software

• PyBrain
• MINUIT

Cites Work

• Unnamed Item
• A first unbiased global NLO determination of parton distributions and their uncertainties
• A determination of parton distributions with faithful uncertainty estimation
• Neural network parameterizations of electromagnetic nucleon form-factors

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