Fitting parton distribution data with multiplicative normalization uncertainties

NNPDF Collaboration, Richard D. Ball, Luigi Del Debbio, Stefano Forte, Alberto Guffanti, Jose I. Latorre, Juan Rojo, Maria Ubiali

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

The extraction of robust parton distribution functions with faithful errors requires a careful treatment of the uncertainties in the experimental results. In particular, the data sets used in current analyses each have a different overall multiplicative normalization uncertainty that needs to be properly accounted for in the fitting procedure. Here we consider the generic problem of performing a global fit to many independent data sets each with a different overall multiplicative normalization uncertainty. We show that the methods in common use to treat multiplicative uncertainties lead to systematic biases. We develop a method which is unbiased, based on a self-consistent iterative procedure. We then apply our generic method to the determination of parton distribution functions with the NNPDF methodology, which uses a Monte Carlo method for uncertainty estimation.

Original languageEnglish
Article number075
Pages (from-to)-
Number of pages34
JournalJournal of High Energy Physics
Issue number5
Publication statusPublished - May 2010

Keywords / Materials (for Non-textual outputs)

  • QCD Phenomenology


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