dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Laenen, E.L.M.P. | |
dc.contributor.author | Hasenack, Toon | |
dc.date.accessioned | 2024-08-07T23:07:53Z | |
dc.date.available | 2024-08-07T23:07:53Z | |
dc.date.issued | 2024 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/47164 | |
dc.description.abstract | We present the first NNPDFpol2.0 results, where we fit polarised parton distribution functions to deep inelastic scattering, W-production in Drell-Yan and (di)jet data including charm contributions at next-to-next leading order. The effect on singlet and non-singlet distributions turns out to be small, however the gluon pPDF is quite significantly altered. We continue by
considering EIC pseudo-data and its implications on the distributions. Next to this, we attempt to lay a strong theoretical foundation of factorisation by using the framework of soft-collinear effective theory. | |
dc.description.sponsorship | Utrecht University | |
dc.language.iso | EN | |
dc.subject | A machine learning analysis of polarised proton substructure | |
dc.title | A machine learning analysis of polarised proton substructure | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | Polarised Parton Distribution Functions, NNPDF, Gluon Spin | |
dc.subject.courseuu | Theoretical Physics | |
dc.thesis.id | 36160 | |