Open AccessNeural network determination of parton distributions: the nonsinglet case
Author(s) -
The NNPDF Collaboration,
Luigi Del Debbio,
Stefano Forte,
José I. Latorre,
Andrea Piccione,
Juan Rojo
Publication year - 2007
Publication title -
journal of high energy physics
Language(s) - English
Resource type - Journals
eISSN - 1126-6708
pISSN - 1029-8479
DOI - 10.1088/1126-6708/2007/03/039
Subject(s) - parton , artificial neural network , construct (python library) , monte carlo method , representation (politics) , computer science , particle physics , distribution (mathematics) , probability distribution , quark , statistical physics , physics , artificial intelligence , mathematics , statistics , mathematical analysis , politics , political science , law , programming language
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. © SISSA 2007
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