EPPS16: Nuclear parton distributions with LHC data

Abstract: We introduce a global analysis of collinearly factorized nuclear parton distribution functions (PDFs) including, for the first time, data constraints from LHC proton-lead collisions. In comparison to our previous analysis, EPS09, where data only from charged-lepton-nucleus deep inelastic scattering (DIS), Drell-Yan (DY) dilepton production in proton-nucleus collisions and inclusive pion production in deuteron-nucleus collisions were the input, we now increase the variety of data constraints to cover also neutrino-nucleus DIS and low-mass DY production in pion-nucleus collisions. The new LHC data significantly extend the kinematic reach of the data constraints. We now allow much more freedom for the flavour dependence of nuclear effects than in other currently available analyses. As a result, especially the uncertainty estimates are more objective flavour by flavour. The neutrino DIS plays a pivotal role in obtaining a mutually consistent behaviour for both up and down valence quarks, and the LHC dijet data clearly constrain gluons at large momentum fraction. Mainly for insufficient statistics, the pion-nucleus DY and heavy gauge boson production in proton-lead collisions impose less visible constraints. The outcome - a new set of next-to-leading order nuclear PDFs called EPPS16 - is made available for applications in high-energy nuclear collisions.

• The paper presents an updated global analysis that incorporates LHC pPb collisions alongside neutrino DIS and pion-induced DY data to improve nuclear PDF accuracy.
• It employs a next-to-leading order QCD framework with flexible parametrization to capture flavor-dependent effects and reduce biases from previous models.
• The study demonstrates a significant reduction in uncertainty for gluon and quark distributions, providing refined predictions for heavy-ion collisions.

EPPS16: Nuclear Parton Distributions with LHC Data

The paper "EPPS16: Nuclear parton distributions with LHC data" presents a comprehensive update to the global analysis of nuclear parton distribution functions (PDFs), integrating LHC proton-lead (pPb) collision data for the first time. This advancement overcomes previous limitations by incorporating a broader scope of experimental constraints beyond the EPS09 framework, specifically addressing potential biases and inaccuracies inherent in earlier nuclear PDF studies.

The authors, Eskola et al., extend the data constraints well beyond those utilized in EPS09 by including novel data sources such as neutrino deep inelastic scattering (DIS) and pion-induced Drell-Yan (DY) processes. Furthermore, they critically integrate data from pPb collisions acquired at the LHC, which offers an enhanced kinematic range to evaluate PDF behaviors. This integration permits a deeper exploration of the flavor dependence of nuclear modifications—an area that had been overly constrained or simplified in previous models.

Key Insights and Methods

Utilizing a next-to-leading order (NLO) perturbative QCD approach, the EPPS16 framework adopts a more flexible parametrization to better capture the flavor-dependent nuclear effects. This modification is essential for a detailed understanding of the spectral behavior of nuclear partons, particularly the gluon distributions that have historically suffered from high uncertainty.

A significant methodological advancement in EPPS16 is the improved handling of valence and sea quarks, leveraging data from neutrino-nucleus interactions to achieve a balanced and consistent treatment for up and down valence quarks. In parallel, LHC dijet datasets provide stringent constraints for the gluon distributions across a wide momentum fraction range, enhancing the reliability of predictions at large $x$ values.

Numerical Results and Implications

EPPS16 demonstrates a noteworthy tightening of uncertainty bands for various partonic components compared to EPS09, thanks to the richer dataset. For instance, incorporating LHC dijet data has substantially reduced the uncertainty in gluon distributions at high $x$, which is pivotal for precision predictions in heavy-ion collisions.

The findings suggest that the nuclear effects—for instance, antishadowing and the EMC effect—are well-aligned across different parton species when analyzed at similar $Q^2$ scales, owing in part to the refined flavor-independence assumption and updated parametrization methods. This leads to implications for theoretical predictions and experimental analyses in high-energy nuclear collisions, particularly in providing orthogonal constraints to those obtained from RHIC pion production data.

Future Directions

The EPPS16 nuclear PDFs stand to benefit from further enhancements with incoming data from the LHC's Run 2 and future experiments like the Electron-Ion Collider (EIC). These will potentially fill the existing gaps in small-$x$ gluon data and provide deeper insight into heavy-flavor physics in nuclear environments.

Additionally, future development could consider upgrades to next-to-next-to-leading order (NNLO) frameworks and the inclusion of QED effects, ensuring greater accuracy alongside the increasing precision of experimental observations. Advanced studies might also explore merging nuclear and free-proton PDF fits to comprehensively address systematics arising from baseline assumptions.

In summary, the EPPS16 analysis by Eskola et al. marks a significant step forward in the accurate representation of nuclear PDFs, informed by a diverse range of modern datasets and methodological innovations. The broader implications bode well for our understanding of partonic interactions in nuclear environments, thereby enhancing predictive capabilities essential for interpreting results from current and future collider experiments.