Bayesian reweighting of nuclear PDFs and constraints from proton-lead collisions at the LHC
Nestor Armesto, Juan Rojo, Carlos A. Salgado, Pia Zurita
TL;DR
The paper addresses constraining nuclear PDFs (nPDFs) with LHC proton–lead data by adopting Bayesian reweighting to update an existing EPS09-based ensemble without performing a full refit. It develops a Monte Carlo EPS09 replica set from Hessian errors, defines replica weights using the new data via a $\chi^2$-based likelihood, and tests consistency with effective replica number $N_{\rm eff}$ and rescaled uncertainties. The analysis applies the method to pseudo-data for low-mass Drell–Yan and charged-hadron production in $p$+Pb collisions and explores a CGC scenario to assess DGLAP–CGC discrimination, finding substantial reductions in uncertainties, particularly for the small-$x$ gluon; the MC EPS09 replicas are released publicly for external validation and use. Overall, the work provides a practical, fast pathway to integrate new hard-scattering data into nPDF constraints and to gauge compatibility with existing nuclear data. The approach enhances the reliability of initial-state characterizations in heavy-ion collisions and offers a usable tool for experimental collaborations.
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.