Abstract
The choice of data that enters a global QCD analysis can have a substantial impact on the resulting parton distributions and their predictions for collider observables. One of the main reasons for this has to do with the possible presence of inconsistencies, either internal within an experiment or external between different experiments. In order to assess the robustness of the global fit, different definitions of a conservative PDF set, that is, a PDF set based on a maximally consistent dataset, have been introduced. However, these approaches are typically affected by theory biases in the selection of the dataset. In this contribution, after a brief overview of recent NNPDF developments, we propose a new, fully objective, definition of a conservative PDF set, based on the Bayesian reweighting approach. Using the new NNPDF3.0 framework, we produce various conservative sets, which turn out to be mutually in agreement within the respective PDF uncertainties, as well as with the global fit. We explore some of their implications for LHC phenomenology, finding also good consistency with the global fit result. These results provide a non-trivial validation test of the new NNPDF3.0 fitting methodology, and indicate that possible inconsistencies in the fitted dataset do not affect substantially the global fit PDFs.
Highlights
The choice of data that enters a global QCD analysis can have a substantial impact on the resulting parton distributions and their predictions for collider observables
The NNPDF2.3 sets can be accessed though the LHAPDF library, and they are available as internal sets in various widely used codes
The leading order version of NNPDF2.3 [8, 9] has been implemented as internal set in the Pythia8 Monte Carlo event generator [10], where it has been used as the basis of the recent Monash 2013 Tune [11] of Pythia8
Summary
The accurate determination of the parton distribution functions (PDFs) of the proton is one of the most important tasks for precision phenomenology at the LHC [1]. Jet data is included in the NNLO fits using the improved threshold approximation [28], validated with the exact NNLO calculation of the gluongluon channel [29], which allows to carefully select only those data points with kinematics for which the threshold approximation is close enough to the exact calculation [30] In this contribution, I want to focus on a particular aspect of the NNPDF3.0 analysis, namely a new proposal for the definition, in a fully objective way, a set of parton distributions based on a maximally consistent dataset, in order to explore the possible impact of dataset inconsistencies in the global fit
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