Mapping the sensitivity of hadronic experiments to nucleon structure

TL;DR

This paper introduces PDFSense, a new statistical tool that quantifies the impact of experimental data on proton PDFs, helping to optimize future measurements and improve understanding of nucleon structure.

Contribution

The paper presents a novel method using Hessian correlation and sensitivity measures, implemented in an open-source package, to evaluate and visualize experimental data's influence on PDFs.

Findings

Inclusive jet production at the LHC has high potential to impact future PDF fits.

PDFSense effectively ranks and visualizes experimental data sensitivity.

Preliminary fits confirm the importance of key measurements.

Abstract

Determinations of the proton's collinear parton distribution functions (PDFs) are emerging with growing precision due to increased experimental activity at facilities like the Large Hadron Collider. While this copious information is valuable, the speed at which it is released makes it difficult to quickly assess its impact on the PDFs, short of performing computationally expensive global fits. As an alternative, we explore new methods for quantifying the potential impact of experimental data on the extraction of proton PDFs. Our approach relies crucially on the Hessian correlation between theory-data residuals and the PDFs themselves, as well as on a newly defined quantity --- the sensitivity --- which represents an extension of the correlation and reflects both PDF-driven and experimental uncertainties. This approach is realized in a new, publicly available analysis package PDFSense, which operates with these statistical measures to identify particularly sensitive experiments, weigh their relative or potential impact on PDFs, and visualize their detailed distributions in a space of the parton momentum fraction $x$ and factorization scale $\mu$. This tool offers a new means of understanding the influence of individual measurements in existing fits, as well as a predictive device for directing future fits toward the highest impact data and assumptions. Along the way, many new physics insights can be gained or reinforced. As one of many examples, PDFSense is employed to rank the projected impact of new LHC measurements in jet, vector boson, and $t\bar{t}$ production and leads us to the conclusion that inclusive jet production at the LHC has a potential for playing an indispensable role in future PDF fits. These conclusions are independently verified by preliminarily fitting this experimental information and investigating the constraints they supply using the Lagrange multiplier technique.