Abstract
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 χ 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.
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associated with one jet at the LHC, Phys. Lett.
B 611 (2005) 60 [Author information
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ArXiv ePrint: 1006.0342
Supported by: the Ministry of Science and Higher Education project DWM/57/T2K/2007 as well as the Polish Ministry of Science Grant project number: NN202 368439. (Krzysztof M. Graczyk)
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Graczyk, K.M., Płonski, P. & Sulej, R. Neural network parameterizations of electromagnetic nucleon form-factors. J. High Energ. Phys. 2010, 53 (2010). https://doi.org/10.1007/JHEP09(2010)053
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DOI: https://doi.org/10.1007/JHEP09(2010)053