REVIEW 8 cited by
The deconvolution problem of deeply virtual Compton scattering
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
The deconvolution problem of deeply virtual Compton scattering
read the original abstract
Generalised parton distributions are instrumental to study both the three-dimensional structure and the energy-momentum tensor of the nucleon, and motivate numerous experimental programmes involving hard exclusive measurements. Based on a next-to-leading order analysis and a careful study of evolution effects, we exhibit non-trivial generalised parton distributions with arbitrarily small imprints on deeply virtual Compton scattering observables. This means that in practice the reconstruction of generalised parton distributions from measurements, known as the deconvolution problem, does not possess a unique solution for this channel. In this Letter we discuss the consequences on the extraction of generalised parton distributions from data and advocate for a multi-channel analysis.
Forward citations
Cited by 8 Pith papers
-
Reconstructing the full kinematic dependence of GPDs from pseudo-distributions
Lattice QCD pseudo-distributions at m_π=358 MeV are inverted via multidimensional Gaussian process regression to reconstruct the full kinematic dependence of GPDs H^{u-d} and E^{u-d} while directly extracting double d...
-
TMDs in the Lens of Generative AI: A Pixel-Based Approach to Partonic Imaging
A nonparametric pixel-based Bayesian method integrates TMD evolution with generative AI and SVD to image parton distributions and reveal null TMDs unconstrained by observables.
-
TMDs in the Lens of Generative AI: A Pixel-Based Approach to Partonic Imaging
A nonparametric pixel-based Bayesian method integrates TMD evolution with generative AI sampling and SVD to extract parton distributions and identify unconstrained null components from multi-scale observables.
-
Exclusive photoproduction of a di-meson pair with large invariant mass
Calculations of di-meson photoproduction amplitudes at leading order show cross sections up to 100 times larger than single-meson cases, enabling better GPD extraction.
-
Exclusive photoproduction of a di-meson pair with large invariant mass
Computes LO amplitudes for di-meson photoproduction channels sensitive to quark GPDs and reports high cross sections suitable for GPD studies at JLab energies.
-
Constraining DVCS Compton Form Factors Using Lattice QCD informed Neural Network
A neural network framework informed by lattice QCD uses all-order dispersion relations to significantly constrain both real and imaginary parts of Compton Form Factors extracted from DVCS proton data.
-
Assessing the impact of the electron ion collider in China on Deeply Virtual Compton Scattering
Projected EicC DVCS asymmetry data would substantially reduce uncertainties on all leading-order Compton form factors, most strongly in the sea-quark region.
-
Three-dimensional imaging of hadrons with hard exclusive reactions: advances in experiment, theory, phenomenology, and lattice QCD
A community white paper reviewing GPD-based 3D imaging of hadrons — experiment, theory, phenomenology, lattice QCD — and the roadmap toward precision tomography at JLab, COMPASS, J-PARC, and future electron-ion colliders.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.