Reconstructing parton distribution functions from Ioffe time data: from Bayesian methods to neural networks
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 establis...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
April 5, 2019
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| In: |
Journal of high energy physics
Year: 2019, Issue: 4 |
| ISSN: | 1029-8479 |
| DOI: | 10.1007/JHEP04(2019)057 |
| Online Access: | Verlag, Volltext: https://doi.org/10.1007/JHEP04(2019)057
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| Author Notes: | Joseph Karpie, Kostas Orginos, Alexander Rothkopf and Savvas Zafeiropoulos |
| Summary: | 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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| Item Description: | Gesehen am 06.06.2019 |
| Physical Description: | Online Resource |
| ISSN: | 1029-8479 |
| DOI: | 10.1007/JHEP04(2019)057 |