NNPDF

NNPDF is the acronym used to identify the parton distribution functions from the NNPDF Collaboration. NNPDF parton densities are extracted from global fits to data based on a combination of a Monte Carlo method for uncertainty estimation and the use of neural networks as basic interpolating functions.

The NNPDF Methodology

The NNPDF approach can be divided into four main steps:

  • The generation of a large sample of Monte Carlo replicas of the original experimental data, in a way that central values, errors and correlations are reproduced with enough accuracy.
  • The training (minimization of the \chi^2) of a set of PDFs parametrized by neural networks on each of the above MC replicas of the data. PDFs are parametrized at the initial evolution scale Q^{2}_{0} and then evolved to the experimental data scale Q^2 by means of the DGLAP equations. Since the PDF parametrization is redundant, the minimization strategy is based in genetic algorithms.
  • The neural network training is stopped dynamically before entering into the overlearning regime, that is, so that the PDFs learn the physical laws which underlie experimental data without fitting simultaneously statistical noise.
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