Combining lattice QCD and phenomenological inputs on generalised parton distributions at moderate skewness.
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| Title: | Combining lattice QCD and phenomenological inputs on generalised parton distributions at moderate skewness. |
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| Authors: | Riberdy, Michael Joseph1 (AUTHOR) michael.riberdy@cea.fr, Dutrieux, Hervé1,2 (AUTHOR), Mezrag, Cédric1 (AUTHOR), Sznajder, Paweł3 (AUTHOR) |
| Source: | European Physical Journal C -- Particles & Fields. Feb2024, Vol. 84 Issue 2, p1-15. 15p. |
| Subjects: | Artificial neural networks, Partons, Quantum chromodynamics, Compton scattering, Data mining, Data extraction |
| Abstract: | We present a systematic study demonstrating the impact of lattice QCD data on the extraction of generalised parton distributions (GPDs). For this purpose, we use a previously developed modelling of GPDs based on machine learning techniques fulfilling the theoretical requirements of polynomiality, a form of positivity constraint and known reduction limits. A special care is given to estimate the uncertainty stemming from the ill-posed character of the connection between GPDs and the experimental processes usually considered to constrain them, like deeply virtual Compton scattering (DVCS). Moke lattice QCD data inputs are included in a Bayesian framework to a prior model based on an Artificial Neural Network. This prior model is fitted to reproduce the most experimentally accessible information of a phenomenological extraction by Goloskokov and Kroll. We highlight the impact of the precision, correlation and kinematic coverage of lattice data on GPD extraction at moderate ξ which has only been brushed in the literature so far, paving the way for a joint extraction of GPDs. [ABSTRACT FROM AUTHOR] |
| Copyright of European Physical Journal C -- Particles & Fields is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 176095788 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Combining lattice QCD and phenomenological inputs on generalised parton distributions at moderate skewness. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Riberdy%2C+Michael+Joseph%22">Riberdy, Michael Joseph</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> michael.riberdy@cea.fr</i><br /><searchLink fieldCode="AR" term="%22Dutrieux%2C+Hervé%22">Dutrieux, Hervé</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mezrag%2C+Cédric%22">Mezrag, Cédric</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sznajder%2C+Paweł%22">Sznajder, Paweł</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22European+Physical+Journal+C+--+Particles+%26+Fields%22">European Physical Journal C -- Particles & Fields</searchLink>. Feb2024, Vol. 84 Issue 2, p1-15. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Partons%22">Partons</searchLink><br /><searchLink fieldCode="DE" term="%22Quantum+chromodynamics%22">Quantum chromodynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Compton+scattering%22">Compton scattering</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Data+extraction%22">Data extraction</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We present a systematic study demonstrating the impact of lattice QCD data on the extraction of generalised parton distributions (GPDs). For this purpose, we use a previously developed modelling of GPDs based on machine learning techniques fulfilling the theoretical requirements of polynomiality, a form of positivity constraint and known reduction limits. A special care is given to estimate the uncertainty stemming from the ill-posed character of the connection between GPDs and the experimental processes usually considered to constrain them, like deeply virtual Compton scattering (DVCS). Moke lattice QCD data inputs are included in a Bayesian framework to a prior model based on an Artificial Neural Network. This prior model is fitted to reproduce the most experimentally accessible information of a phenomenological extraction by Goloskokov and Kroll. We highlight the impact of the precision, correlation and kinematic coverage of lattice data on GPD extraction at moderate ξ which has only been brushed in the literature so far, paving the way for a joint extraction of GPDs. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of European Physical Journal C -- Particles & Fields is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=176095788 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1140/epjc/s10052-024-12513-2 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Partons Type: general – SubjectFull: Quantum chromodynamics Type: general – SubjectFull: Compton scattering Type: general – SubjectFull: Data mining Type: general – SubjectFull: Data extraction Type: general Titles: – TitleFull: Combining lattice QCD and phenomenological inputs on generalised parton distributions at moderate skewness. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Riberdy, Michael Joseph – PersonEntity: Name: NameFull: Dutrieux, Hervé – PersonEntity: Name: NameFull: Mezrag, Cédric – PersonEntity: Name: NameFull: Sznajder, Paweł IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 14346044 Numbering: – Type: volume Value: 84 – Type: issue Value: 2 Titles: – TitleFull: European Physical Journal C -- Particles & Fields Type: main |
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