Reconstructing parton distribution functions from Ioffe time data: from Bayesian methods to neural networks

Karpie, Joseph; Orginos, Kostas; Rothkopf, Alexander; Zafeiropoulos, Savvas

doi:10.1007/JHEP04(2019)057

Title: Reconstructing parton distribution functions from Ioffe time data: from Bayesian methods to neural networks

Journal Article · 05 April 2019 · Journal of High Energy Physics (Online)

DOI: https://doi.org/10.1007/JHEP04(2019)057 · OSTI ID:1524153

Karpie, Joseph ^[1]; Orginos, Kostas ^[1]; Rothkopf, Alexander ^[2]; Zafeiropoulos, Savvas ^[3]

College of William and Mary, Williamsburg, VA (United States). Dept. of Physics; Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
Univ. of Stavanger (Norway). Faculty of Science and Technology
Heidelberg Univ. (Germany). Inst. for Theoretical Physics

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 high energy nuclear physics. In this study, we present and evaluate the efficiency of several numerical methods, well established in the study of inverse problems, to reconstruct the full x-dependence of PDFs. Our starting point are the so called Ioffe time PDFs, which are accessible from Euclidean time simulations 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.

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Research Organization:: Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States); College of William and Mary, Williamsburg, VA (United States); Heidelberg Univ. (Germany); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

Sponsoring Organization:: USDOE Office of Science (SC), Nuclear Physics (NP); USDOE Office of Science (SC), Workforce Development for Teachers and Scientists (WDTS); National Science Foundation (NSF); German Research Foundation (DFG); Science and Technology Facilities Council (STFC) (United Kingdom)

Grant/Contract Number:: AC05-06OR23177; FG02-04ER41302; SC0014664; AC02-05CH11231; PHY-1626177; SFB 1225; ST/P000681/1

OSTI ID:: 1524153

Alternate ID(s):: OSTI ID: 1542915

Report Number(s):: JLAB-THY-19-2898; DOE/OR/23177-4647; arXiv:1901.05408

Journal Information:: Journal of High Energy Physics (Online), Vol. 2019, Issue 4; ISSN 1029-8479

Publisher:: Springer BerlinCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 75 works

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