Bayesian reweighting of nuclear PDFs and constraints from proton-lead collisions at the LHC

Abstract: New hard-scattering measurements from the LHC proton-lead run have the potential to provide important constraints on the nuclear parton distributions and thus contributing to a better understanding of the initial state in heavy ion collisions. In order to quantify these constraints, as well as to assess the compatibility with available nuclear data from fixed target experiments and from RHIC, the traditional strategy is to perform a global fit of nuclear PDFs. This procedure is however time consuming and technically challenging, and moreover can only be performed by the PDF fitters themselves. In the case of proton PDFs, an alternative approach has been suggested that uses Bayesian inference to propagate the effects of new data into the PDFs without the need of refitting. In this work, we apply this reweighting procedure to study the impact on nuclear PDFs of low-mass Drell-Yan and single-inclusive hadroproduction pseudo-data from proton-lead collisions at the LHC as representative examples. In the hadroproduction case, in addition we assess the possibility of discriminating between the DGLAP and CGC production frameworks. We find that the LHC proton-lead data could lead to a substantial reduction of the uncertainties on nuclear PDFs, in particular for the small-x gluon PDF where uncertainties could decrease by up to a factor two. The Monte Carlo replicas of EPS09 used in the analysis are released as a public code for general use. It can be directly used, in particular, by the experimental collaborations to check, in a straightforward manner, the degree of compatibility of the new data with the global nPDF analyses.