Abstract
We present a model-independent determination of the nuclear parton distribution functions (nPDFs) using machine learning methods and Monte Carlo techniques based on the NNPDF framework. The neutral-current deep-inelastic nuclear structure functions used in our previous analysis, nNNPDF1.0, are complemented by inclusive and charm-tagged cross-sections from charged-current scattering. Furthermore, we include all available measurements of W and Z leptonic rapidity distributions in proton-lead collisions from ATLAS and CMS at sqrt{s} = 5.02 TeV and 8.16 TeV. The resulting nPDF determination, nNNPDF2.0, achieves a good description of all datasets. In addition to quantifying the nuclear modifications affecting individual quarks and antiquarks, we examine the implications for strangeness, assess the role that the momentum and valence sum rules play in nPDF extractions, and present predictions for representative phenomenological applications. Our results, made available via the LHAPDF library, highlight the potential of high-energy collider measurements to probe nuclear dynamics in a robust manner.
Highlights
Precise extractions of nuclear parton distribution functions (nPDFs) are crucial to study the strong interaction in the high-density regime, but are necessary to model the initial state of heavy ion collisions which aim to characterize the Quark-Gluon Plasma (QGP) [4, 5] using hard probes
We present a model-independent determination of the nuclear parton distribution functions using machine learning methods and Monte Carlo techniques based on the NNPDF framework
We first study the features of the nNNPDF2.0 fit by assessing the quality of its agreement with experimental data, focusing largely on the LHC weak boson production cross-sections, and by studying the behavior of nuclear modification ratios across different nuclei
Summary
We provide details of the experimental measurements used as input for the nNNPDF2.0 determination. An emphasis is made in particular on the new datasets that are added with respect to those that were present in nNNPDF1.0. We discuss the theoretical calculations corresponding to these datasets and their numerical implementation in our fitting framework
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