The neural network approach to parton fitting

We introduce the neural network approach to global fits of parton distribution functions. First we review previous work on unbiased parametrizations of deep-inelastic structure functions with faithful estimation of their uncertainties, and then we summarize the current status of neural network parto...

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Bibliographic Details
Published in:AIP conference proceedings 2005-10, Vol.792 (1)
Main Authors: Rojo, Joan, Latorre, Jose I., Del Debbio, Luigi, Forte, Stefano, INFN, Sezione di Milano, Piccione, Andrea, INFN Sezione di Torino
Format: Article
Language:English
Subjects:
DEEP INELASTIC SCATTERING
DISTRIBUTION
DISTRIBUTION FUNCTIONS
GLUONS
NEURAL NETWORKS
PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
QUANTUM CHROMODYNAMICS
QUARKS
REVIEWS
STRUCTURE FUNCTIONS
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Description
Summary:We introduce the neural network approach to global fits of parton distribution functions. First we review previous work on unbiased parametrizations of deep-inelastic structure functions with faithful estimation of their uncertainties, and then we summarize the current status of neural network parton distribution fits.
ISSN:0094-243X
1551-7616
DOI:10.1063/1.2122059