Pith. sign in

REVIEW 9 cited by

Towards a fitting procedure for deeply virtual Compton scattering at next-to-leading order and beyond

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

arxiv hep-ph/0703179 v2 pith:LKEGF3AX submitted 2007-03-16 hep-ph

classification hep-ph
keywords conformalcomptonorderscatteringvirtualaccuracyamplitudedeeply
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Combining dispersion and operator product expansion techniques, we derive the conformal partial wave decomposition of the virtual Compton scattering amplitude in terms of complex conformal spin to twist-two accuracy. The perturbation theory predictions for the deeply virtual Compton scattering (DVCS) amplitude are presented in next-to-leading order for both conformal and modified minimal subtraction scheme. Within a conformal subtraction scheme, where we exploit predictive power of conformal symmetry, the radiative corrections are presented up to next-to-next-to-leading order accuracy. Here, because of the trace anomaly, the mixing of conformal moments of generalized parton distributions (GPD) at the three-loop level remains unknown. Within a new proposed parameterization for GPDs, we then study the convergence of perturbation theory and demonstrate that our formalism is suitable for a fitting procedure of DVCS observables.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Kinematic power corrections to DVCS to twist-six accuracy

    hep-ph 2025-01 accept novelty 8.0 of 10

    Complete kinematic power corrections up to twist-6 are derived for nucleon DVCS, and the series converges best when organized in powers of 1/(Q²+t).

  2. Conformal moments of the two-loop coefficient functions in DVCS

    hep-ph 2025-12 unverdicted novelty 7.0 of 10

    Conformal moments of the two-loop DVCS coefficient functions have been computed with a new technique.

  3. Benchmarking the Nearside Energy-Energy Correlators with Mellin Transform

    hep-ph 2026-07 conditional novelty 6.0 of 10

    A Mellin-transform framework with one fitted transition scale Λ describes nearside EECs in e+e− annihilation at NNLO+NNLL accuracy across ALEPH and earlier data.

  4. GUMP1.0 -- First global extraction of generalized parton distributions from experiment and lattice data with NLO accuracy

    hep-ph 2025-09 conditional novelty 6.0 of 10

    A new global extraction, GUMP1.0, fits generalized parton distributions to 2,646 experimental and lattice data points at NLO accuracy and uses them to image the proton and decompose its spin.

  5. Implications of exclusive photon leptoproduction measurements for the proton charge-radius puzzle

    hep-ph 2026-07 conditional novelty 5.0 of 10

    After excluding or cutting low-|t| CLAS 2018 data, BH-dominated EP measurements yield a proton charge radius smaller than the PDG average and consistent with PRad and muonic hydrogen.

  6. Constraining DVCS Compton Form Factors Using Lattice QCD informed Neural Network

    hep-ph 2026-06 unverdicted novelty 5.0 of 10

    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.

  7. Mechanical properties of the nucleon from the generalized parton distributions

    hep-ph 2025-01 conditional novelty 5.0 of 10

    Using a double-distribution GPD model constrained by elastic-scattering data, the paper fits DQ(0) = -3.37 ± 0.17 from Compton form factors and derives proton pressure, shear, and radii.

  8. Sensitivity of Double Deeply Virtual Compton Scattering observables to GPDs

    nucl-ex 2024-12 conditional novelty 5.0 of 10

    Under assumed upgraded luminosity, the DDVCS asymmetries ALU, ALL, AC_UU (JLab) and ALU, AC_UU, AUT (EIC) are predicted to be measurable and to discriminate among VGG, GK19, KM, and AFKM12 GPD models.

  9. Compton Form Factor Extraction using Quantum Deep Neural Networks

    cs.LG 2025-04 unverdicted novelty 4.0 of 10

    Quantum-inspired deep neural networks extract Compton form factors from JLab data with higher predictive accuracy and tighter uncertainties than classical DNNs on pseudodata benchmarks, then applied to real measurements.

Pith tools