2021
DOI: 10.1007/jhep02(2021)138
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Neural-network analysis of Parton Distribution Functions from Ioffe-time pseudodistributions

Abstract: 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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Cited by 50 publications
(27 citation statements)
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“…In order to distinguish these objects from their light-cone analogs, one usually refers to such space-like correlation functions as pseudo-PDFs (e.g. [10]) or quasi-PDFs (e.g. [11]), depending on the actual implementation.…”
Section: Introductionmentioning
confidence: 99%
“…In order to distinguish these objects from their light-cone analogs, one usually refers to such space-like correlation functions as pseudo-PDFs (e.g. [10]) or quasi-PDFs (e.g. [11]), depending on the actual implementation.…”
Section: Introductionmentioning
confidence: 99%
“…The quality of data, however, hints that a future calculation of a distribution less constrained by experiment may benefit from the use of distillation. By validating the distillation method in the unpolarized nucleon PDF, our study opens new avenues of synergy between lattice and phenomenology in the spirit of [11,99,100]. We performed a careful study of the correct extraction of the matrix elements that yield the pseudo-ITD and we analyzed in detail its real and imaginary components.…”
Section: Discussionmentioning
confidence: 99%
“…In previous lattice PDF studies [34,65,74,75,80,86,87,110], the chosen functional forms are similar to those used in phenomenological analyses of PDFs [123][124][125][126]. Progress has also been made on the application of neural networks to parameterize the PDF [67,99,127]. In this work, all of the unknown functions, q − (x), q + (x), P 1 (ν), R 1 (ν), and B 1 (ν), are parameterized using Jacobi polynomials.…”
Section: Parameterization Of Unknown Functionsmentioning
confidence: 99%
“…If sufficiently many distinct models are used, the possible biases from choices of model can be averaged away through this AICc weighted average. For example, including fits which use neural networks, which were performed for lattice PDFs in [67,99], which likely would have distinct model dependent biases from the Jacobi polynomials fits. Unfortunately in this preliminary study which only uses Jacobi polynomial based models, the systematic errors may not be sufficiently distinct.…”
Section: Model Weighted Averagesmentioning
confidence: 99%