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}
}
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… 

Unbinned multivariate observables for global SMEFT analyses from machine learning

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.

AI and Theoretical Particle Physics

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

Machine Learning and LHC Event Generation

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

Parton distributions from LHC, HERA, Tevatron and fixed target data: MSHT20 PDFs

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,

Parton distributions and lattice-QCD calculations: Toward 3D structure

Towards a new generation of parton densities with deep learning models

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.

Monotone Piecewise Cubic Interpolation

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

Parton distributions from high-precision collider data

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

A determination of the fragmentation functions of pions, kaons, and protons with faithful uncertainties

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

The asymptotic behaviour of parton distributions at small and large x

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.

PDF4LHC recommendations for LHC Run II

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

Bootstrap Methods: Another Look at the Jackknife

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,