A nonparametric pixel-based Bayesian method integrates TMD evolution with generative AI sampling and SVD to extract parton distributions and identify unconstrained null components from multi-scale observables.
A Higher Order Perturbative Parton Evolution Toolkit (HOPPET)
6 Pith papers cite this work. Polarity classification is still indexing.
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abstract
This document describes a Fortran 95 package for carrying out DGLAP evolution and other common manipulations of parton distribution functions (PDFs). The PDFs are represented on a grid in x-space so as to avoid limitations on the functional form of input distributions. Good speed and accuracy are obtained through the representation of splitting functions in terms of their convolution with a set of piecewise polynomial basis functions, and Runge-Kutta techniques are used for the evolution in Q. Unpolarised evolution is provided to NNLO, including heavy-quark thresholds in the MSbar scheme, and longitudinally polarised evolution to NLO. The code is structured so as to provide simple access to the objects representing splitting functions and PDFs, making it possible for a user to extend the facilities already provided. A streamlined interface is also available, facilitating use of the evolution part of the code from F77 and C/C++.
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UNVERDICTED 6representative citing papers
Non-singlet fragmentation functions of pions and kaons are determined at NNLO QCD from charge asymmetry measurements in e+e- annihilation and SIDIS, yielding a scaling index of 0.7 and strangeness suppression of 0.5.
A framework based on linear response and influence functions maps data sensitivities in global QCD analyses to show how experiments determine central values, uncertainties, and correlations of non-perturbative functions.
Pion PDFs from holographic light-front QCD are consistent with global analyses and produce J/ψ cross sections matching experimental data across energies and targets.
A Chebyshev interpolation method for efficient numerical evolution of PDFs and DPDs via DGLAP equations at NNLO with flavor matching and independent scales for DPDs.
Light-cone quark model PDFs for kaon and heavy mesons are evolved via NLO DGLAP to predict EIC structure functions and COMPASS Drell-Yan cross sections while showing heavy constituents dominate momentum fractions.
citing papers explorer
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TMDs in the Lens of Generative AI: A Pixel-Based Approach to Partonic Imaging
A nonparametric pixel-based Bayesian method integrates TMD evolution with generative AI sampling and SVD to extract parton distributions and identify unconstrained null components from multi-scale observables.
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Determination of Fragmentation Functions from Charge Asymmetries in Hadron Production
Non-singlet fragmentation functions of pions and kaons are determined at NNLO QCD from charge asymmetry measurements in e+e- annihilation and SIDIS, yielding a scaling index of 0.7 and strangeness suppression of 0.5.
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Mapping data sensitivities in global QCD analysis with linear response and influence functions
A framework based on linear response and influence functions maps data sensitivities in global QCD analyses to show how experiments determine central values, uncertainties, and correlations of non-perturbative functions.
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Pion parton distribution functions and pion-nucleus induced $J/\psi$ production in extended light-front holographic QCD
Pion PDFs from holographic light-front QCD are consistent with global analyses and produce J/ψ cross sections matching experimental data across energies and targets.
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Efficient interpolation and evolution of parton distribution functions
A Chebyshev interpolation method for efficient numerical evolution of PDFs and DPDs via DGLAP equations at NNLO with flavor matching and independent scales for DPDs.
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An Analysis on the Parton Distribution Functions of Heavy Mesons
Light-cone quark model PDFs for kaon and heavy mesons are evolved via NLO DGLAP to predict EIC structure functions and COMPASS Drell-Yan cross sections while showing heavy constituents dominate momentum fractions.