PDFSense: Mapping the sensitivity of hadronic experiments to nucleon structure

TL;DR

PDFSense is a new analysis tool that quantifies how sensitive hadronic experimental data are to the proton's internal structure, helping optimize future measurements and understand existing data's impact on parton distribution functions.

Contribution

The paper introduces PDFSense, a novel method and software for assessing the impact of experimental data on proton PDFs using correlation and sensitivity measures.

Findings

Identifies experiments with high sensitivity to PDFs

Provides visualization of data impact across x and μ

Enables predictive analysis for future data impact

Abstract

Recent high precision experimental data from a variety of hadronic processes opens new opportunities for determination of the collinear parton distribution functions (PDFs) of the proton. In fact, the wealth of information from experiments such as the Large Hadron Collider (LHC) and others, makes it difficult to quickly assess the 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 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.