A Guide to Light-Cone PDFs from Lattice QCD: An Overview of Approaches, Techniques, and Results

Abstract EN

Within the theory of Quantum Chromodynamics (QCD), the rich structure of hadrons can be quantitatively characterized, among others, using a basis of universal nonperturbative functions: parton distribution functions (PDFs), generalized parton distributions (GPDs), transverse momentum dependent parton distributions (TMDs), and distribution amplitudes (DAs).

For more than half a century, there has been a joint experimental and theoretical effort to obtain these partonic functions.

However, the complexity of the strong interactions has placed severe limitations, and first-principle information on these distributions was extracted mostly from their moments computed in Lattice QCD.

Recently, breakthrough ideas changed the landscape and several approaches were proposed to access the distributions themselves on the lattice.

In this paper, we review in considerable detail approaches directly related to partonic distributions.

We highlight a recent idea proposed by X.

Ji on extracting quasidistributions that spawned renewed interest in the whole field and sparked the largest amount of numerical studies within Lattice QCD.

We discuss theoretical and practical developments, including challenges that had to be overcome, with some yet to be handled.

We also review numerical results, including a discussion based on evolving understanding of the underlying concepts and the theoretical and practical progress.

Particular attention is given to important aspects that validated the quasidistribution approach, such as renormalization, matching to light-cone distributions, and lattice techniques.

In addition to a thorough discussion of quasidistributions, we consider other approaches: hadronic tensor, auxiliary quark methods, pseudodistributions, OPE without OPE, and good lattice cross-sections.

In the last part of the paper, we provide a summary and prospects of the field, with emphasis on the necessary conditions to obtain results with controlled uncertainties.