Neural network determination of parton distributions: the nonsinglet case

Details Neural network determination of parton distributions: the nonsinglet case Neural network determination of parton distributions: the nonsinglet case

2007-01-16

arxiv.org/abs/hep-ph/0701127v1

JHEP 0703:039,2007

Physics

High Energy Physics

High Energy Physics - Phenomenology

46 pages, 18 figures, LaTeX with JHEP3 class

Scientific paper

10.1088/1126-6708/2007/03/039

We provide a determination of the isotriplet quark distribution from available deep--inelastic data using neural networks. We give a general introduction to the neural network approach to parton distributions, which provides a solution to the problem of constructing a faithful and unbiased probability distribution of parton densities based on available experimental information. We discuss in detail the techniques which are necessary in order to construct a Monte Carlo representation of the data, to construct and evolve neural parton distributions, and to train them in such a way that the correct statistical features of the data are reproduced. We present the results of the application of this method to the determination of the nonsinglet quark distribution up to next--to--next--to--leading order, and compare them with those obtained using other approaches.

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