1 sujet IRFU/DPhN

Dernière mise à jour : 21-01-2021


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• Theoretical Physics

 

A simultaneous determination of parton-distribution and fragmentation functions using artificial neural networks

SL-DRF-21-0317

Research field : Theoretical Physics
Location :

Service de Physique Nucléaire (DPhN)

Laboratoire structure du nucléon (LSN) (LSN)

Saclay

Contact :

Valerio Bertone

Hervé Moutarde

Starting date : 01-10-2021

Contact :

Valerio Bertone
CEA - DRF/IRFU/DPhN/LSN


Thesis supervisor :

Hervé Moutarde
CEA - DRF/IRFU/SPhN/Théorie Hadronique

33 1 69 08 73 88

Laboratory link : http://irfu.cea.fr/Phocea/Vie_des_labos/Ast/ast_groupe.php?id_groupe=4189

The general goal of this project is a better understanding of the internal structure of hadrons. This problem is addressed in the context of Quantum Chromodynamics (QCD) whose basic building blocks are quarks and gluons. Useful information concerning the hadronic structure is thus encoded in the so-called parton distribution functions (PDFs), that describe how hadrons turn into quarks and gluons, and in the fragmentation functions (FFs), that instead describe how quarks and gluons turn into hadrons. Due to the fact that QCD is strongly coupled at energies of the order of the typical hadronic mass, PDFs and FFs cannot be computed from first principles using perturbation theory. A common solution to this problem consists of paramterising PDFs and FFs and determining them from fits to experimental data. So far, most of the PDF and FF determinations have been obtained separately considering experimental data that are sensitive to only one of them. The subject of this project is a simultaneous determination of PDFs and FFs. The advantage of such a simultaneous determination is a better exploitation of the experimental data that will eventually lead to a better knowledge of PDFs and FFs and thus of the hadronic structure. Given the complexity of the task, PDFs and FFs will be parameterised in terms of artificial neural networks (ANNs). The use of ANNs helps reduce the parametric bias leading to a more accurate determination of PDFs and FFs.

 

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