# A data-based parametrization of parton distribution functions

@article{Carrazza2021ADP,
title={A data-based parametrization of parton distribution functions},
author={Stefano Carrazza and Juan Cruz-Martinez and Roy Stegeman},
journal={The European Physical Journal C},
year={2021},
volume={82}
}
• Published 4 November 2021
• Computer Science
• The European Physical Journal C
Since the first determination of a structure function many decades ago, all methodologies used to determine structure functions or parton distribution functions (PDFs) have employed a common prefactor as part of the parametrization. The NNPDF collaboration pioneered the use of neural networks to overcome the inherent bias of constraining the space of solution with a fixed functional form while still keeping the same common prefactor as a preprocessing. Over the years various, increasingly…
3 Citations
• Physics
• 2022
A flexible open source framework, ML4EFT, is developed, enabling the integration of unbinned multivariate observables into global SMEFT fits, and combines machine learning regression and classification techniques to parameterize high-dimensional likelihood ratios.
• Physics
• 2022
Theoretical particle physicists continue to push the envelope in both high performance computing and in managing and analyzing large data sets. For example, the goals of sub-percent accuracy in
• Physics
• 2022
First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and

## References

SHOWING 1-10 OF 19 REFERENCES

• Physics
The European Physical Journal C
• 2021
We present the new MSHT20 set of parton distribution functions (PDFs) of the proton, determined from global analyses of the available hard scattering data. The PDFs are made available at NNLO, NLO,
• 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.
• Mathematics
• 1980
In a 1980 paper [SIAM J. Numer. Anal., 17 (1980), pp. 238–246] the authors developed a univariate piecewise cubic interpolation algorithm which produces a monotone interpolant to monotone data. This
• Physics
The European physical journal. C, Particles and fields
• 2017
We present a new set of parton distributions, NNPDF3.1, which updates NNPDF3.0, the first global set of PDFs determined using a methodology validated by a closure test. The update is motivated by
• Physics
The European physical journal. C, Particles and fields
• 2017
We present NNFF1.0, a new determination of the fragmentation functions (FFs) of charged pions, charged kaons, and protons/antiprotons from an analysis of single-inclusive hadron production data in
• Physics
The European physical journal. C, Particles and fields
• 2016
It is found that for valence distributions both Regge theory and counting rules are confirmed, at least within uncertainties, while for sea quarks and gluons the results are less conclusive.
• Physics
• 2015
We provide an updated recommendation for the usage of sets of parton distribution functions (PDFs) and the assessment of PDF and PDF+$\alpha_s$ uncertainties suitable for applications at the LHC Run
We discuss the following problem given a random sample X = (X 1, X 2,…, X n) from an unknown probability distribution F, estimate the sampling distribution of some prespecified random variable R(X,