PDF bias and flavor dependence in TMD distributions

Journal of High Energy Physics

Regular Article - Theoretical Physics

Francesco Hautmann¹  Ignazio Scimemi²  Alexey Vladimirov³  Pia Zurita⁴  Marcin Bury⁵  Sergio Leal-Gomez⁶ 

[1] CERN, Theory Department, 1211, Geneva, CH, Switzerland;Elementaire Deeltjes Fysica, Universiteit Antwerpen, B 2020, Antwerpen, Belgium;Theoretical Physics Department, University of Oxford, OX1 3PU, Oxford, U.K.;Dpto. de Física Teórica & IPARCOS, Universidad Complutense de Madrid, E-28040, Madrid, Spain;Dpto. de Física Teórica & IPARCOS, Universidad Complutense de Madrid, E-28040, Madrid, Spain;Institut für Theoretische Physik, Universität Regensburg, D-93040, Regensburg, Germany;Institut für Theoretische Physik, Universität Regensburg, D-93040, Regensburg, Germany;Jerzy Haber Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences, Niezapominajek 8, 30-239, Kraków, Poland;Wien Universität, Faculty of Physics, Boltzmanngasse 5, A-1090, Vienna, Austria;

关键词: Parton Distributions;    Resummation;    Factorization;    Renormalization Group;   

DOI  :  10.1007/JHEP10(2022)118

 received in 2022-02-21, accepted in 2022-10-06,  发布年份 2022

来源: Springer

【 摘 要 】

Transverse momentum dependent (TMD) distributions match collinear parton density functions (PDF) in the limit of small transverse distances, which is accounted for by global extractions of TMD distributions. We study the influence of the collinear PDF value and uncertainties on the determination of unpolarized TMD distributions and the description of Drell-Yan (DY) and Z-boson production measurements at low transverse momenta. We take into account, for the first time in fits including TMD evolution, flavor-dependent non-perturbative TMD profiles. We carry out a Bayesian analysis to incorporate the propagation of PDF uncertainties into TMD extractions. We find that collinear PDF uncertainties and non-perturbative TMD flavor dependence are both essential to obtain reliable TMD determinations, and should be included in future global analyses.

【 授权许可】

Unknown   
© The Author(s) 2022

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