# Parton Distribution Functions

@article{Forte2022PartonDF,
title={Parton Distribution Functions},
author={Stefano Forte and Joey Huston and Robert Samuel Thorne and Stefano Carrazza and Jun Gao and Zahari Kassabov and Pavel M. Nadolsky and Juan Rojo},
journal={Artificial Intelligence for High Energy Physics},
year={2022}
}
• Published 7 October 2016
• Computer Science
• Artificial Intelligence for High Energy Physics
We discuss the determination of the parton substructure of hadrons by casting it as a peculiar form of pattern recognition problem in which the pattern is a probability distribution, and we present the way this problem has been tackled and solved. Specifically, we review the NNPDF approach to PDF determination, which is based on the combination of a Monte Carlo approach with neural networks as basic underlying interpolators. We discuss the current NNPDF methodology, based on genetic…
9 Citations

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## References

SHOWING 1-10 OF 55 REFERENCES
Towards a new generation of parton densities with deep learning models
• Computer Science
The European Physical Journal C
• 2019
A new regression model for the determination of parton distribution functions (PDF) using techniques inspired from deep learning projects and a new efficient computing framework based on graph generated models for PDF parametrization and gradient descent optimization is implemented.
Neural network determination of parton distributions: the nonsinglet case
• Mathematics
• 2007
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
Update on neural network parton distributions : NNPDF1.1
• Physics
• 2008
We present recent progress within the NNPDF parton analysis framework. After a brief review of the results from the DIS NNPDF analysis, NNPDF1.0, we discuss results from an updated analysis with
Neural network parametrization of deep inelastic structure functions
• Computer Science
• 2002
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
Unbiased determination of the proton structure function F 2 p with faithful uncertainty estimation
• Computer Science
• 2005
We construct a parametrization of the deep-inelastic structure function of the proton F2(x,Q2) based on all available experimental information from charged lepton deep-inelastic scattering
Algorithms for Hyper-Parameter Optimization
• Computer Science
NIPS
• 2011
This work contributes novel techniques for making response surface models P(y|x) in which many elements of hyper-parameter assignment (x) are known to be irrelevant given particular values of other elements.
Parton distributions with theory uncertainties: general formalism and first phenomenological studies
• Physics
The European Physical Journal C
• 2019
We formulate a general approach to the inclusion of theoretical uncertainties, specifically those related to the missing higher order uncertainty (MHOU), in the determination of parton distribution
Uncertainties of predictions from parton distribution functions. II. The Hessian method
• Physics
• 2001
We develop a general method to quantify the uncertainties of parton distribution functions and their physical predictions, with emphasis on incorporating all relevant experimental constraints. The
Fitting parton distribution data with multiplicative normalization uncertainties
• Mathematics
• 2009
The extraction of robust parton distribution functions with faithful errors requires a careful treatment of the uncertainties in the experimental results. In particular, the data sets used in current