Delineating the saturation boundary: linear vs non-linear QCD evolution from HERA data to LHC phenomenology

TL;DR

This paper investigates the applicability of non-linear QCD evolution equations versus linear DGLAP evolution in describing small-x DIS data from HERA, with implications for heavy ion collisions and LHC phenomenology.

Contribution

It compares rcBK non-linear evolution fits with NNLO DGLAP fits on HERA data, establishing the kinematic region where each model is valid and constraining saturation models for heavy ion collision initial states.

Findings

rcBK accurately describes data within specific kinematic regions

DGLAP fits with kinematic cuts can mimic saturation effects

Constraints on saturation models for heavy ion initial conditions

Abstract

The forthcoming p+Pb run at the LHC will provide crucial in formation on the initial state effects of heavy ion collisions and on the gluon saturation phenomena. In turn, most of the saturation inspired phenomenology in heavy ion collisions borrows substantial empiric information from the analysis of e+p data, where abundant high quality data on the small-x kinematic region is available. Indeed, the very precise combined HERA data provides a testing ground in which the relevance of novel QCD regimes, other than the successful linear DGLAP evolution, in small-x inclusive DIS data can be ascertained. We present a study of the dependence of the AAMQS fits, based on the running coupling BK non-linear evolution equations (rcBK), on the fitted dataset. This allows for the identification of the kinematical region where rcBK accurately describes the data, and thus for the determination of its applicability boundary. It also set important constraints to the saturation models used to model the early stages of heavy ion collisions. Finally we compare the rcBK results with NNLO DGLAP fits, obtained with the NNPDF methodology with analogous kinematical cuts. Further, we explore the impact on LHC phenomenology of applying stringent kinematical cuts to the low-x HERA data in a DGLAP fit.