Fast interpolation grids for the Drell-Yan process

TL;DR

This paper introduces a new interpolation grid for the Drell-Yan process at NNLO, enabling rapid theory predictions crucial for proton structure studies, and evaluates its accuracy and utility.

Contribution

It presents the first interpolation grids for the Drell-Yan process at NNLO, bridging a gap in tools for PDF analysis and enabling faster computations.

Findings

The grids facilitate quick re-evaluation of predictions for various PDFs.

They reveal cancellations between partonic channels at NNLO.

The grids assess the validity of the K-factor approximation.

Abstract

Modern analyses of experimental data from hadron colliders rely on theory predictions at high orders in perturbation theory and a variety of input settings. Interpolation grids facilitate an almost instant re-evaluation of theory predictions for different input parton distributions functions (PDFs) or scale settings and are thus indispensable in the study of the parton content of the proton. While interpolation grids at next-to-next-to-leading order (NNLO) exist for some key processes relevant for PDF determinations, a notable exception is the Drell-Yan process that constitutes the production of electroweak gauge bosons at hadron colliders and provides important constraints on the quark content of the proton. To address this gap, we report on a new interface between the parton-level Monte Carlo generator NNLOJET and the interpolation grid library PINEAPPL and demonstrate its use for the Drell-Yan process. Accompanying this note, we release Drell-Yan grids covering a wide range of measurements that commonly enter global determinations of PDFs. We use the grids to study accidental cancellation between partonic channels at NNLO and inspect the validity of a K-factor approximation that was widely employed previously.