Parton distribution functions from Ioffe time pseudo-distributions

Joó, Bálint; Karpie, Joseph; Orginos, Kostas; Radyushkin, Anatoly; Richards, David; Zafeiropoulos, Savvas

doi:10.1007/jhep12(2019)081

Cited by 100 publications

(103 citation statements)

References 71 publications

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“…Numerically, it is very close to the nucleon rest-frame matrix element of Ref. [77]. As argued in Ref.…”

Section: Comparison With Other Determinationssupporting

confidence: 86%

“…For completeness, some key points of the method are highlighted below. We refer the readers to [38,[75][76][77] for more details on the implementation of this method.…”

Section: Methodsmentioning

confidence: 99%

“…The lowest four moments will be extracted from the a127m415L ensemble data by inverting the Vandermonde matrix as was performed in [64,77]. The imaginary and real components are used individually to calculate the odd and even moments respectively.…”

Section: Moments Of the Pion Pdfmentioning

confidence: 99%

“…Just like in Ref. [77], for the matching of the reduced pseudo-ITD to the MS ITD at a particular scale µ, we perform an inversion of Eq. (5) simply by switching Q(ν, µ) and M(ν, z 2 ) and changing the sign of α s .…”

Section: Extraction Of the Pion Valence Distributionmentioning

confidence: 99%

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Pion valence structure from Ioffe-time parton pseudodistribution functions

et al. 2019

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We present a calculation of the pion valence quark distribution extracted using the formalism of reduced Ioffe time pseudo-distributions or more commonly known as pseudo-PDFs. Our calculation is carried out on two different 2+1 flavor QCD ensembles using the isotropic-clover fermion action, with lattice dimensions 24 3 × 64 and 32 3 × 96 at the lattice spacing of a = 0.127 fm, and with the quark mass equivalent to a pion mass of mπ 415 MeV. We incorporate several combinations of smeared-point and smeared-smeared pion source-sink interpolation fields in obtaining the lattice QCD matrix elements using the summation method. After one-loop perturbative matching and combining the pseudo-distributions from these two ensembles, we extract the pion valence quark distribution using a phenomenological functional form motivated by the global fits of parton distribution functions. We also calculate the lowest four moments of the pion quark distribution through the "OPE without OPE". We present a qualitative comparison between our lattice QCD extraction of the pion valence quark distribution with that obtained from global fits and previous lattice QCD calculations.arXiv:1909.08517v1 [hep-lat]

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“…Numerically, it is very close to the nucleon rest-frame matrix element of Ref. [77]. As argued in Ref.…”

Section: Comparison With Other Determinationssupporting

confidence: 86%

“…For completeness, some key points of the method are highlighted below. We refer the readers to [38,[75][76][77] for more details on the implementation of this method.…”

Section: Methodsmentioning

confidence: 99%

Section: Moments Of the Pion Pdfmentioning

confidence: 99%

Section: Extraction Of the Pion Valence Distributionmentioning

confidence: 99%

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Pion valence structure from Ioffe-time parton pseudodistribution functions

et al. 2019

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“…In this work, we are interested in finding out how well the ML approach would work with the difficult computational situation in LaMETtype observables and whether computational cost can be saved for future large lattices (say, 64 3 × 192 and above). Although this paper focuses on the discussion with quasi-PDF, what we learn here also applies to the pseudo-PDF 1 [27][28][29][30] correlators since the building blocks of matrix elements are the same.…”

Section: Introductionmentioning

confidence: 99%

Machine-learning prediction for quasiparton distribution function matrix elements

Zhang

Fan

et al. 2020

Phys. Rev. D

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There have been rapid developments in the direct calculation in lattice QCD (LQCD) of the Bjorken-x dependence of hadron structure through large-momentum effective theory (LaMET). LaMET overcomes the previous limitation of LQCD to moments (that is, integrals over Bjorken x) of hadron structure, allowing LQCD to directly provide the kinematic regions where the experimental values are least known. LaMET requires large-momentum hadron states to minimize its systematics and allow us to reach small-x reliably. This means that very fine lattice spacing to minimize lattice artifacts at order ðP z aÞ n will become crucial for next-generation LaMET-like structure calculations. Furthermore, such calculations require operators with long Wilson-link displacements, especially in finer lattice units, increasing the communication costs relative to that of the propagator inversion. In this work, we explore whether machinelearning algorithms can make predictions of correlators to reduce the computational cost of these LQCD calculations. We consider two algorithms, gradient-boosting decision tree and linear models, applied to LaMET data, the matrix elements needed to determine the kaon and η s unpolarized parton distribution functions (PDFs), meson distribution amplitude (DA), and the nucleon gluon PDF. We find that both algorithms can reliably predict the target observables with different prediction accuracy and systematic errors. The predictions from smaller displacement z to larger ones work better than those for momentum p due to the higher correlation among the data.

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Neural-network analysis of Parton Distribution Functions from Ioffe-time pseudodistributions

Debbio

Giani

Karpie

et al. 2021

J. High Energ. Phys.

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We extract two nonsinglet nucleon Parton Distribution Functions from lattice QCD data for reduced Ioffe-time pseudodistributions. We perform such analysis within the NNPDF framework, considering data coming from different lattice ensembles and dis- cussing in detail the treatment of the different source of systematics involved in the fit. We introduce a recipe for taking care of systematics and use it to perform our extraction of light-cone PDFs.

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Parton distribution functions from Ioffe time pseudo-distributions

Cited by 100 publications

References 71 publications

Pion valence structure from Ioffe-time parton pseudodistribution functions

Pion valence structure from Ioffe-time parton pseudodistribution functions

Machine-learning prediction for quasiparton distribution function matrix elements

Neural-network analysis of Parton Distribution Functions from Ioffe-time pseudodistributions

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