Neural network determination of parton distributions: the nonsinglet case

The NNPDF Collaboration, Luigi Del Debbio, Stefano Forte, Jose latorre, Andrea Piccione, Joan Rojo

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Pages (from-to)-
Number of pages45
JournalJournal of High Energy Physics
Issue number039
Publication statusPublished - 16 Jan 2007

Keywords / Materials (for Non-textual outputs)

  • hep-ph
  • Parton Model
  • Deep Inelastic Scattering
  • QCD


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