Reconstructing parton distribution functions from Ioffe time data: from Bayesian methods to neural networks
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Publication:2421640
DOI10.1007/JHEP04(2019)057zbMATH Open1415.81056arXiv1901.05408OpenAlexW3105162519WikidataQ128077896 ScholiaQ128077896MaRDI QIDQ2421640
Author name not available (Why is that?)
Publication date: 17 June 2019
Published in: (Search for Journal in Brave)
Abstract: The computation of the parton distribution functions (PDF) or distribution amplitudes (DA) of hadrons from first principles lattice QCD constitutes a central open problem. In this study, we present and evaluate the efficiency of a selection of methods for inverse problems to reconstruct the full -dependence of PDFs. Our starting point are the so called Ioffe time PDFs, which are accessible from Euclidean time calculations in conjunction with a matching procedure. Using realistic mock data tests, we find that the ill-posed incomplete Fourier transform underlying the reconstruction requires careful regularization, for which both the Bayesian approach as well as neural networks are efficient and flexible choices.
Full work available at URL: https://arxiv.org/abs/1901.05408
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