Neural network parameterizations of electromagnetic nucleon form-factors

被引：13

作者：

Graczyk, Krzysztof M. ^{[1
]}

Plonski, Piotr ^{[2
]}

Sulej, Robert ^{[3
]}

机构：

[1] Univ Wroclaw, Inst Theoret Phys, PL-50204 Wroclaw, Poland

[2] Warsaw Univ Technol, Inst Radioelect, PL-00665 Warsaw, Poland

[3] A Soltan Inst Nucl Studies, PL-00681 Warsaw, Poland

来源：

JOURNAL OF HIGH ENERGY PHYSICS | 2010年 / 09期

关键词：

Lepton-Nucleon Scattering; MULTILAYER FEEDFORWARD NETWORKS; SQUARED 4-MOMENTUM TRANSFERS; ELECTRON-DEUTERON SCATTERING; MOMENTUM-TRANSFER; QUARK-MODEL; PROTON; FM-2; STRANGENESS; PHYSICS; VECTOR;

D O I：

10.1007/JHEP09(2010)053

中图分类号：

O412 [相对论、场论]; O572.2 [粒子物理学];

学科分类号：

摘要：

The electromagnetic nucleon form-factors data are studied with artificial feed forward neural networks. As a result the unbiased model-independent form-factor parametrizations are evaluated together with uncertainties. The Bayesian approach for the neural networks is adapted for chi(2) error-like function and applied to the data analysis. The sequence of the feed forward neural networks with one hidden layer of units is considered. The given neural network represents a particular form-factor parametrization. The so-called evidence (the measure of how much the data favor given statistical model) is computed with the Bayesian framework and it is used to determine the best form factor parametrization.

引用

页数：30

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