Neural network parametrization of deep inelastic structure functions

@article{Forte2002NeuralNP,
  title={Neural network parametrization of deep inelastic structure functions},
  author={Stefano Forte and Llu{\'i}s Garrido and Jos{\'e} Ignacio Latorre and Andrea Piccione},
  journal={Journal of High Energy Physics},
  year={2002},
  volume={2002},
  pages={062-062}
}
We construct a parametrization of deep-inelastic structure functions which retains information on experimental errors and correlations, and which does not introduce any theoretical bias while interpolating between existing data points. We generate a Monte Carlo sample of pseudo-data configurations and we train an ensemble of neural networks on them. This effectively provides us with a probability measure in the space of structure functions, within the whole kinematic region where data are… 

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