Publications
Publication Information
| Title | Reconstructing parton distribution functions from Ioffe time data: from Bayesian methods to Neural Networks |
| Authors | Joseph Karpie, Konstantinos Orginos, Alexander Rothkopf, Savvas Zafeiropoulos |
| JLAB number | JLAB-THY-19-2898 |
| LANL number | arXiv:1901.05408 |
| Other number | DOE/OR/23177-4647 |
| Document Type(s) | (Journal Article) |
| Associated with EIC: | No |
| Supported by Jefferson Lab LDRD Funding: | No |
| Funding Source: | Nuclear Physics (NP) |
|
Journal Compiled for Journal of High Energy Physics Volume 19 Issue 04 Page(s) 057 Refereed | |
| Publication 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 $x$-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. |
| Experiment Numbers: | |
| Group: | THEORY CENTER |
| Document: | |
| DOI: | https://doi.org/10.1007/JHEP04(2019)057 |
| Accepted Manuscript: | Karpie2019_Article_ReconstructingPartonDistributi.pdf |
| Supporting Documents: | |
| Supporting Datasets: | |