Parton distributions with scale uncertainties: a MonteCarlo sampling approach

TL;DR

This paper introduces MCscales, a Monte Carlo-based method for incorporating scale uncertainties into parton distribution functions, enabling more accurate predictions for LHC observables by accounting for correlations between PDFs and scale variations.

Contribution

The paper extends the Monte Carlo sampling approach to include scale uncertainties, providing a new way to match scale variations with PDF uncertainties in a correlated manner.

Findings

Exact matching of scale variations in PDFs and cross sections

Enables comprehensive exploration of scale uncertainties in phenomenology

Provides enriched PDF sets with scale information

Abstract

We present the MCscales approach for incorporating scale uncertainties in parton distribution functions (PDFs). The new methodology builds on the Monte Carlo sampling for propagating experimental uncertainties into the PDF space that underlies the NNPDF approach, but it extends it to the space of factorisation and renomalisation scales. A prior probability is assigned to each scale combinations set in the theoretical predictions used to obtain each PDF replica in the Monte Carlo ensemble and a posterior probability is obtained by selecting replicas that satisfy fit-quality criteria. Our approach allows one to exactly match the scale variations in the PDFs with those in the computation of the partonic cross sections, thus accounting for the full correlations between the two. We illustrate the opportunities for phenomenological exploration made possible by our methodology for a variety of LHC observables. Sets of PDFs enriched with scale information are provided, along with a set of tools to use them.