Fitting parton distribution data with multiplicative normalization uncertainties

The NNPDF Collaboration, Richard 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 number75
JournalJournal of High Energy Physics
Volume2010
Issue number75
Early online date20 May 2010
DOIs
Publication statusPublished - 20 May 2010

Keywords / Materials (for Non-textual outputs)

  • QCD Phenomenology

Fingerprint

Dive into the research topics of 'Fitting parton distribution data with multiplicative normalization uncertainties'. Together they form a unique fingerprint.

Cite this