Analysis of proton-lead data via re-weighting

The recent proton-lead run at the LHC shall provide new information on the partonic behaviour within the nuclear medium. At LHC energies the dominant contribution comes from gluon-initiated processes that is, the least well constrained parton density. Therefore it is important to profit from any information that new data can provide us. A time-saving alternative to performing a global fit is the use of Bayesian inference, a powerful tool to realize the impact of data into a set of PDFs independently of the original fitters. In this work we apply the Bayesian re-weighting technique to analyze pseudo data for LHC kinematics in Drell-Yan and hadro-production processes. A set of Monte Carlo replicas for EPS09 is released in a public code for general use.

CERN LHC Coll
p nucleus: scattering
nuclear matter
gluon: distribution function
parton: density
parton: distribution function
numerical calculations: Monte Carlo
statistical analysis: Bayesian
Drell-Yan process
hadron: production

References(20)

Figures(0)

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