Hadronization Corrections to Jet Cross Sections in Deep-Inelastic Scattering

TL;DR

The paper quantifies non-perturbative hadronization corrections to jet cross sections in deep-inelastic scattering using fragmentation models. It compares four jet definitions, highlighting that the inclusive k_T algorithm yields the smallest corrections with minimal model dependence, and demonstrates that these corrections are typically under 10% for high-E_T jets. By contrasting parton cascade models with NLO predictions across angular distributions, jet substructure, and radius dependence, the authors assess the viability of applying hadronization corrections to NLO calculations in the absence of a formal infrared matching. The results show that hadronization uncertainties are on par with or smaller than NLO uncertainties for the inclusive k_T definition, supporting precise perturbative QCD tests in DIS, while emphasizing the need for proper hadronization-NLO matching in the future.

Abstract

The size of non-perturbative corrections to high E_T jet production in deep-inelastic scattering is reviewed. Based on predictions from fragmentation models, hadronization corrections for different jet definitions are compared and the model dependence as well as the dependence on model parameters is investigated. To test whether these hadronization corrections can be applied to next-to-leading order (NLO) calculations, jet properties and topologies in different parton cascade models are compared to those in NLO. The size of the uncertainties in estimating the hadronization corrections is compared to the uncertainties of perturbative predictions. It is shown that for the inclusive k_\perp ordered jet clustering algorithm the hadronization corrections are smallest and their uncertainties are of the same size as the uncertainties of perturbative NLO predictions.

Figures (7)

• Figure 1: The hadronization corrections to the dijet cross section for different jet definitions as a function of $Q^2$ as predicted by the HERWIG cluster fragmentation model.
• Figure 2: Hadronization corrections for differential dijet distributions for the inclusive $k_\perp$ algorithm as predicted from different models.
• Figure 3: The hadronization corrections to the $\overline{E}_T$ (left) and to the $\xi$ distributions (right) for the dijet cross section defined by the inclusive $k_\perp$ algorithm. Shown are the predictions from the LEPTO/JETSET model with default parameter settings (line) and the changes obtained by parameter variations as described in the text (dotted lines).
• Figure 4: Higher order corrections to jet pseudorapidity distributions for the forward and the backward jet in the HERA laboratory frame, the forward jet in the Breit frame and the average pseudorapidity of the dijet system. Displayed are the predictions from the leading-order matrix elements (LO) and those including higher order corrections from either the next-to-leading order (NLO) calculation, or as given by parton showers (HERWIG, LEPTO) or the dipole cascade (ARIADNE) for the inclusive $k_\perp$ algorithm. Positive pseudorapidities are towards the proton direction in both the laboratory and the Breit frame.
• Figure 5: Subjet multiplicities for an inclusive jet sample defined by the inclusive $k_\perp$ algorithm. Compared are the predictions of different parton cascade models to the ${\cal O}(\alpha_s^2)$ calculation.