Snowmass 2021 whitepaper: Proton structure at the precision frontier

TL;DR

This whitepaper reviews the current status and future directions for high-precision parton distribution functions essential for high-energy physics experiments, emphasizing advancements in experimental, theoretical, and computational methods.

Contribution

It outlines the roadmap for achieving N2LO and N3LO accuracy in PDFs and discusses new challenges and opportunities in systematic control, experimental-theoretical synergies, and innovative analysis techniques.

Findings

Progress towards N2LO and N3LO PDF accuracy.

Identification of systematic control challenges.

Potential for integrating machine learning in PDF analysis.

Abstract

An overwhelming number of theoretical predictions for hadron colliders require parton distribution functions (PDFs), which are an important ingredient of theory infrastructure for the next generation of high-energy experiments. This whitepaper summarizes the status and future prospects for determination of high-precision PDFs applicable in a wide range of energies and experiments, in particular in precision tests of the Standard Model and in new physics searches at the high-luminosity Large Hadron Collider and Electron-Ion Collider. We discuss the envisioned advancements in experimental measurements, QCD theory, global analysis methodology, and computing that are necessary to bring unpolarized PDFs in the nucleon to the N2LO and N3LO accuracy in the QCD coupling strength. Special attention is given to the new tasks that emerge in the era of the precision PDF analysis, such as those focusing on the robust control of systematic factors both in experimental measurements and theoretical computations. Various synergies between experimental and theoretical studies of the hadron structure are explored, including opportunities for studying PDFs for nuclear and meson targets, PDFs with electroweak contributions or dependence on the transverse momentum, for incisive comparisons between phenomenological models for the PDFs and computations on discrete lattice, and for cross-fertilization with machine learning/AI approaches. [Submitted to the US Community Study on the Future of Particle Physics (Snowmass 2021).]