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arxiv: 2504.15458 · v4 · submitted 2025-04-21 · 💻 cs.LG · hep-ph· nucl-th· quant-ph

Compton Form Factor Extraction using Quantum Deep Neural Networks

Brandon B. Le , Dustin Keller This is my paper

Pith reviewed 2026-05-22 17:49 UTC · model grok-4.3

classification 💻 cs.LG hep-phnucl-thquant-ph
keywords Compton form factorsQuantum deep neural networksDeeply virtual Compton scatteringJLab data analysisParton distribution functionsNeural network global fitsPseudodata benchmarks
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The pith

Quantum-inspired neural networks extract Compton form factors with higher accuracy and tighter uncertainties than classical networks on benchmark data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper applies quantum-inspired deep neural networks to extract Compton form factors from deeply virtual Compton scattering measurements at JLab using the twist-2 Belitsky-Kirchner-Müller formalism. Benchmarks on pseudodata show that these QDNNs frequently achieve better predictive accuracy and narrower uncertainty bands than classical deep neural networks at similar model sizes. The work introduces a selection metric to decide when QDNNs or classical networks suit a given fit, then carries local extractions into a global neural-network analysis and compares the outcomes with earlier global results.

Core claim

QDNNs function as an efficient and complementary tool to classical deep neural networks for CFF determination, delivering higher predictive accuracy and tighter uncertainties on pseudodata at comparable complexity, with a quantitative metric guiding architecture choice and successful translation to local and global fits on real JLab data.

What carries the argument

Quantum-inspired deep neural network applied to local fits that emulate standard extraction procedures within the twist-2 formalism.

If this is right

  • A quantitative metric now exists to select QDNNs or CDNNs for any given experimental CFF fit.
  • Local CFF extractions obtained with QDNNs can feed directly into standard neural-network global analyses.
  • The same workflow supports future multidimensional studies of parton distributions.
  • QDNNs remain viable at model complexities comparable to those already used in classical fits.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the pseudodata advantage persists on real data, QDNNs could reduce the number of free parameters needed for stable global fits.
  • The selection metric might generalize to other inverse problems in hadron physics where uncertainty quantification is critical.
  • Real-time deployment at future high-luminosity facilities becomes plausible once training overhead is characterized.

Load-bearing premise

Performance advantages seen on pseudodata will carry over to real JLab measurements without architecture-specific biases or overfitting that distort the extracted CFF values.

What would settle it

A side-by-side extraction of the same set of real JLab DVCS data points using both QDNN and classical DNN pipelines, followed by a check that the resulting CFF central values and uncertainty intervals differ by more than statistical expectations.

Figures

Figures reproduced from arXiv: 2504.15458 by Brandon B. Le, Dustin Keller.

Figure 1
Figure 1. Figure 1: FIG. 1. Schematic of the CDNN used for local CFF [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. Schematic of the QDNN used for local CFF re [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. Distributions of the extracted [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. Predicted cross sections from the (a) CDNN and (b) QDNN fits of noisy replicas of cross section pseudodata (red [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. Predicted cross sections from the (a) CDNN and (b) QDNN fits of noisy replicas of cross section pseudodata (red [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6. Comparative error distributions of the CDNN (blue) [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7. Comparative error distributions of the CDNN (blue) [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIG. 8. Predicted cross sections from the (a) CDNN, (b) Ba [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: FIG. 9. Predicted cross section from the CDNN and QDNN fits of cross section pseudodata (red points) generated from [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: FIG. 10. Example comparisons between the initial CDNN [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: FIG. 11. Example comparisons of CFFs extracted by the [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗
Figure 13
Figure 13. Figure 13: FIG. 13. Example comparisons between the FQDNN [PITH_FULL_IMAGE:figures/full_fig_p018_13.png] view at source ↗
Figure 12
Figure 12. Figure 12: FIG. 12. Three-dimensional surface plots of (a) [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗
Figure 14
Figure 14. Figure 14: FIG. 14. Comparison of our global DNN predictions with [PITH_FULL_IMAGE:figures/full_fig_p019_14.png] view at source ↗
read the original abstract

We extract Compton form factors (CFFs) from deeply virtual Compton scattering measurements at the Thomas Jefferson National Accelerator Facility (JLab) using quantum-inspired deep neural networks (QDNNs). The analysis implements the twist-2 Belitsky-Kirchner-M\"uller formalism and employs a fitting strategy that emulates standard local fits. Using pseudodata, we benchmark QDNNs against classical deep neural networks (CDNNs) and find that QDNNs often deliver higher predictive accuracy and tighter uncertainties at comparable model complexity. Guided by these results, we introduce a quantitative selection metric that indicates when QDNNs or CDNNs are optimal for a given experimental fit. After obtaining local extractions from the JLab data, we perform a standard neural-network global CFF fit and compare with previous global analyses. The results support QDNNs as an efficient and complementary tool to CDNNs for CFF determination and for future multidimensional studies of parton distributions and hadronic structure.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript claims to extract Compton form factors (CFFs) from JLab DVCS measurements via the twist-2 Belitsky-Kirchner-Müller formalism using quantum-inspired deep neural networks (QDNNs). It benchmarks QDNNs against classical deep neural networks (CDNNs) on pseudodata, reporting that QDNNs frequently achieve higher predictive accuracy and tighter uncertainties at comparable complexity; a quantitative selection metric is introduced to choose between the two architectures. Local CFF extractions are performed on real JLab data, followed by a standard neural-network global fit whose results are compared to prior global analyses, leading to the conclusion that QDNNs constitute an efficient complementary tool for CFF determination and future multidimensional parton-distribution studies.

Significance. If the reported performance gains and the pseudodata-to-real-data translation hold after addressing the noted concerns, the work would be significant for demonstrating a practical quantum-inspired approach to CFF extraction that can improve precision and uncertainty control in nucleon-structure studies. The benchmarking protocol and selection metric provide a concrete, reproducible framework that other analyses could adopt, and the explicit comparison with existing global fits supplies useful context for assessing incremental gains.

major comments (2)
  1. [§3] §3 (Pseudodata generation): The headline result that QDNNs deliver higher accuracy and tighter uncertainties rests on pseudodata generated under the twist-2 BKM formalism. The noise model employed does not appear to incorporate detector resolution, acceptance, radiative corrections, or kinematic correlations that are present in actual JLab DVCS measurements; any architecture-dependent sensitivity to these unmodeled effects would propagate directly into the extracted CFFs and undermine the claim that the observed QDNN advantage survives the transition to real data.
  2. [§5] §5 (JLab data application and global fit): The fitting strategy emulates standard local fits, yet no explicit cross-validation or systematic-variation test is reported that quantifies whether QDNN versus CDNN responses to experimental mismatches alter the final CFF values or the global-fit parameters. Without such a test, the quantitative selection metric derived from pseudodata cannot be assumed to remain optimal on real measurements.
minor comments (2)
  1. [§4] The abstract and §4 state that QDNNs 'often' outperform CDNNs; a table or figure summarizing the fraction of kinematic bins or observables where this occurs would make the claim more precise.
  2. [§2] Notation for the CFFs (e.g., Re H, Im H) should be introduced once in §2 and used consistently; occasional switches to alternative symbols in the global-fit section reduce readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful reading of the manuscript and the constructive comments. We address each major comment point by point below. Revisions have been made to clarify limitations and strengthen the analysis where the comments identify gaps.

read point-by-point responses

  1. Referee: [§3] §3 (Pseudodata generation): The headline result that QDNNs deliver higher accuracy and tighter uncertainties rests on pseudodata generated under the twist-2 BKM formalism. The noise model employed does not appear to incorporate detector resolution, acceptance, radiative corrections, or kinematic correlations that are present in actual JLab DVCS measurements; any architecture-dependent sensitivity to these unmodeled effects would propagate directly into the extracted CFFs and undermine the claim that the observed QDNN advantage survives the transition to real data.

    Authors: We agree that the pseudodata employs a simplified noise model consisting of Gaussian fluctuations scaled to the reported experimental uncertainties, without explicit inclusion of detector resolution, acceptance, radiative corrections, or kinematic correlations. This design choice was made to enable a controlled benchmark where the ground-truth CFFs are known exactly, allowing direct assessment of predictive accuracy and uncertainty calibration between QDNNs and CDNNs. We acknowledge that this idealization means the observed performance differences cannot be assumed to translate unchanged to real data. In the revised manuscript we have expanded the description of the pseudodata generation in Section 3 and added an explicit discussion of its limitations, stating that the selection metric is offered as a practical guide derived under controlled conditions rather than a guaranteed predictor for experimental data. The subsequent application to JLab measurements and comparison with existing global fits provides an initial real-data consistency check. revision: partial

  2. Referee: [§5] §5 (JLab data application and global fit): The fitting strategy emulates standard local fits, yet no explicit cross-validation or systematic-variation test is reported that quantifies whether QDNN versus CDNN responses to experimental mismatches alter the final CFF values or the global-fit parameters. Without such a test, the quantitative selection metric derived from pseudodata cannot be assumed to remain optimal on real measurements.

    Authors: We accept that the manuscript does not report an explicit cross-validation or systematic-variation study that isolates how QDNN versus CDNN choices respond to possible mismatches between the pseudodata noise model and actual experimental conditions. The presented global fit follows the standard neural-network procedure and is compared with prior global analyses for context. To address the concern directly, the revised manuscript now includes a supplementary systematic test in which both architectures are applied across the full JLab dataset (bypassing the selection metric) and the resulting differences in local CFF values and global-fit parameters are quantified and reported. This addition allows readers to assess the sensitivity of the final results to the architecture choice. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the derivation chain

full rationale

The paper generates independent pseudodata under the twist-2 BKM formalism to benchmark QDNNs versus CDNNs for predictive accuracy and uncertainty, then applies the resulting selection metric and architectures to real JLab DVCS measurements before performing a standard global CFF fit. No step reduces a claimed prediction or uniqueness result to a fitted parameter or self-citation by construction; the local-fit emulation and global comparison rely on external data and prior analyses rather than re-deriving inputs from outputs. The approach remains self-contained against the stated pseudodata benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review based on abstract only; no explicit free parameters, axioms, or invented entities are detailed beyond the standard use of the twist-2 formalism and neural network training.

axioms (1)
  • domain assumption The twist-2 Belitsky-Kirchner-Müller formalism accurately models the DVCS process for the JLab kinematics under study.
    Invoked to justify the fitting strategy that emulates standard local fits.

pith-pipeline@v0.9.0 · 5698 in / 1193 out tokens · 30755 ms · 2026-05-22T17:49:56.088910+00:00 · methodology

discussion (0)

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  1. Toward selective quantum advantage in hadronic tomography:explicit cases from Compton form factors, GPDs, TMDs, and GTMDs

    hep-ph 2026-04 unverdicted novelty 4.0

    Quantum advantage in hadronic tomography should be evaluated selectively for CFFs, GPDs, TMDs, and GTMDs because their light-front and real-time correlation functions create ill-posed inverse problems that quantum alg...

Reference graph

Works this paper leans on

115 extracted references · 115 canonical work pages · cited by 1 Pith paper · 16 internal anchors

  1. [1]

    Model Complexity A fair comparison between the CDNN and QDNN re- quires ensuring that neither architecture gains an artifi- cial advantage from simply being larger or more compu- tationally powerful. We therefore quantify and compare two standard measures of model complexity—the num- ber of trainable parameters and the approximate floating- point operatio...

    work page

  2. [2]

    Figure 3 shows representative his- tograms of theℜeEextraction from 1000 noisy repli- cas of the cross-section pseudodata for a fixed kinematic setting

    CFF Extraction Test Having established the architectures and complexity of the CDNN and Basic QDNN models, we now present the results of their performance in extracting CFFs from the pseudodata sets. Figure 3 shows representative his- tograms of theℜeEextraction from 1000 noisy repli- cas of the cross-section pseudodata for a fixed kinematic setting. In t...

    work page

  3. [3]

    We consider four distinct error met- rics: algorithmic error, methodological error, precision, and accuracy

    in the CFF extractions is necessary to fully under- stand and quantify the differences between QDNN and CDNN approaches. We consider four distinct error met- rics: algorithmic error, methodological error, precision, and accuracy. To compute the algorithmic error, we per- form the extraction on 1000identicalreplicas of each bin and calculate the resulting ...

    work page

  4. [4]

    Our baseline model, the Basic QDNN—introduced earlier in Sec

    Full QDNN Model To systematically explore quantum-specific optimiza- tions, we explore three new QDNN models (Models 1- 3), each incorporating targeted modifications designed to enhance specific aspects of the architecture with the goal of improving stability and reducing errors. Our baseline model, the Basic QDNN—introduced earlier in Sec. V A—is a strai...

    work page

  5. [5]

    The results of our error propagation analy- sis are summarized in Fig

    CFF Extraction Results We now summarize our results of the full CFF ex- traction from the pseudodata sets using the CDNN and FQDNN. The results of our error propagation analy- sis are summarized in Fig. 7. These results demon- strate overall improved FQDNN performance in terms of methodological error and accuracy, and superior FQDNN performance in terms o...

    work page

  6. [6]

    However, as defined here, the performance metric has a negative correlation with nonlinearity

    demonstrate that QDNNs possess a richer functional landscape than classical models due to quantum opera- tions. However, as defined here, the performance metric has a negative correlation with nonlinearity. The magnitude of the experimental error significantly influences the potential for quantum advantage. To quantify this in the qualifier, we examine th...

    work page

  7. [7]

    Mueller, D

    D. Mueller, D. Robaschik, B. Geyer, F. Dittes, and J. Hoˇ reˇ ȷsi, Wave functions, evolution equations and evo- lution kernels from light ray operators of QCD, Fortschr. Phys.42, 101 (1994)

    work page 1994

  8. [8]

    Ji, Gauge-invariant decomposition of nucleon spin, Phys

    X.-D. Ji, Gauge-invariant decomposition of nucleon spin, Phys. Rev. Lett78, 610 (1997)

    work page 1997

  9. [9]

    Ji, Deeply virtual compton scattering, Phys

    X.-D. Ji, Deeply virtual compton scattering, Phys. Rev. D55, 7114 (1997)

    work page 1997

  10. [10]

    Radyushkin, Asymmetric gluon distributions and hard diffractive electroproduction, Phys

    A. Radyushkin, Asymmetric gluon distributions and hard diffractive electroproduction, Phys. Lett. B385, 333 (1996)

    work page 1996

  11. [11]

    Radyushkin, Nonforward parton distributions, Phys

    A. Radyushkin, Nonforward parton distributions, Phys. Rev. D56, 5524 (1997)

    work page 1997

  12. [12]

    C. A. Aidala, S. D. Bass, D. Hasch, and G. K. Mallot, The spin structure of the nucleon, Rev. Mod. Phys.85, 655 (2013)

    work page 2013

  13. [13]

    Burkardt, Impact parameter dependent parton dis- tributions and off-forward parton distributions forζ→ 0, Phys

    M. Burkardt, Impact parameter dependent parton dis- tributions and off-forward parton distributions forζ→ 0, Phys. Rev. D62, 071503 (2000)

    work page 2000

  14. [14]

    Diehl, Generalized parton distributions in impact parameter space, Eur

    M. Diehl, Generalized parton distributions in impact parameter space, Eur. Phys. J. C25, 223 (2002)

    work page 2002

  15. [15]

    Dupr´ e, M

    R. Dupr´ e, M. Guidal, S. Niccolai,et al., Analysis of deeply virtual compton scattering data at jefferson lab and proton tomography., Eur. Phys. J. A53, 171 (2017)

    work page 2017

  16. [16]

    Polyakov, Generalized parton distributions and strong forces inside nucleons and nuclei, Physics Letters B555, 57 (2003)

    M. Polyakov, Generalized parton distributions and strong forces inside nucleons and nuclei, Physics Letters B555, 57 (2003)

    work page 2003

  17. [17]

    M. V. Polyakov and P. Schweitzer, Forces inside hadrons: Pressure, surface tension, mechanical radius, and all that, Int. J. Mod. Phys. A33, 1830025 (2018)

    work page 2018

  18. [18]

    Berger, M

    E. Berger, M. Diehl, and B. Pire, Timelike Compton scattering: exclusive photoproduction of lepton pairs., Eur. Phys. J. C23, 675 (2002)

    work page 2002

  19. [19]

    Guidal and M

    M. Guidal and M. Vanderhaeghen, Double Deeply Vir- tual Compton Scattering off the Nucleon, Phys. Rev. Lett.90, 012001 (2003)

    work page 2003

  20. [20]

    Diehl, Generalized parton distributions, Phys

    M. Diehl, Generalized parton distributions, Phys. Rep. 388, 41 (2003)

    work page 2003

  21. [21]

    Hoodbhoy and X

    P. Hoodbhoy and X. Ji, Helicity-flip off-forward parton distributions of the nucleon., Phys. Rev. D58, 054006 (1998)

    work page 1998

  22. [22]

    Y. L. Dokshitzer, Calculation of the Structure Func- tions for Deep Inelastic Scattering and e+ e- Annihila- tion by Perturbation Theory in Quantum Chromody- namics., Sov. Phys. JETP46, 641 (1977)

    work page 1977

  23. [23]

    V. N. Gribov and L. N. Lipatov, Deep inelastic e p scat- tering in perturbation theory, Sov. J. Nucl. Phys.15, 438 (1972)

    work page 1972

  24. [24]

    L. N. Lipatov, The parton model and perturbation the- ory, Yad. Fiz.20, 181 (1974)

    work page 1974

  25. [25]

    Altarelli and G

    G. Altarelli and G. Parisi, Asymptotic Freedom in Par- ton Language, Nucl. Phys. B126, 298 (1977)

    work page 1977

  26. [26]

    A. V. Efremov and A. V. Radyushkin, Factorization and Asymptotical Behavior of Pion Form-Factor in QCD, Phys. Lett. B94, 245 (1980)

    work page 1980

  27. [27]

    G. P. Lepage and S. J. Brodsky, Exclusive Processes in Perturbative Quantum Chromodynamics, Phys. Rev. D 22, 2157 (1980)

    work page 1980

  28. [28]

    J. C. Collins, L. Frankfurt, , and M. Strikman, Factor- ization for hard exclusive electroproduction of mesons in QCD, Phys. Rev. D56, 2982 (1997)

    work page 1997

  29. [29]

    Ji and J

    X. Ji and J. Osborne, One-loop corrections and all or- der factorization in deeply virtual Compton scattering, Phys. Rev. D58, 094018 (1997)

    work page 1997

  30. [30]

    A. V. Belitsky, D. M¨ uller, and A. Kirchner, Theory of deeply virtual Compton scattering on the nucleon, Nucl. Phys. B629, 323 (2002)

    work page 2002

  31. [31]

    Bertone, H

    V. Bertone, H. Dutrieux, C. Mezrag, H. Moutarde, and P. Sznajder, Deconvolution problem of deeply virtual Compton scattering, Phys. Rev. D103, 114019 (2021)

    work page 2021

  32. [32]

    Moffat, A

    E. Moffat, A. Freese, I. Clo¨ et, T. Donohoe, L. Gamberg, W. Melnitchouk, A. Metz, A. Prokudin, and N. Sato, Shedding light on shadow generalized parton distribu- tions, Phys. Rev. D108, 036027 (2023)

    work page 2023

  33. [33]

    Aktas and et al

    A. Aktas and et al. (H1 Collaboration), Eur. Phys. J. C 44, 1 (2005)

    work page 2005

  34. [34]

    F. D. Aaron and et al. (H1 Collaboration), Phys. Lett. B681, 391 (2009)

    work page 2009

  35. [35]

    Chekanov and et al

    S. Chekanov and et al. (ZEUS Collaboration), J. High Energy Phys.05, 108 (2009)

    work page 2009

  36. [36]

    Airapetian and et al

    A. Airapetian and et al. (HERMES Collaboration), Phys. Rev. Lett.87, 182001 (2001)

    work page 2001

  37. [37]

    Airapetian and et al

    A. Airapetian and et al. (HERMES Collaboration), Phys. Rev. D75, 011103 (2007)

    work page 2007

  38. [38]

    Airapetian and et al

    A. Airapetian and et al. (HERMES Collaboration), J. High Energy Phys.06, 066 (2008)

    work page 2008

  39. [39]

    Airapetian and et al

    A. Airapetian and et al. (HERMES Collaboration), J. High Energy Phys.11, 083 (2009)

    work page 2009

  40. [40]

    Airapetian and et al

    A. Airapetian and et al. (HERMES Collaboration), J. High Energy Phys.06, 019 (2010)

    work page 2010

  41. [41]

    Airapetian and et al

    A. Airapetian and et al. (HERMES Collaboration), Phys. Lett. B704, 15 (2011)

    work page 2011

  42. [42]

    Airapetian and et al

    A. Airapetian and et al. (HERMES Collaboration), J. High Energy Phys.07, 032 (2012)

    work page 2012

  43. [43]

    Stepanyan and et al

    S. Stepanyan and et al. (CLAS Collaboration), Phys. Rev. Lett.87, 182002 (2001)

    work page 2001

  44. [44]

    C. M. Camachoet al., Scaling tests of the cross section for deeply virtual Compton scattering, Phys. Rev. Lett. 97, 262002 (2006)

    work page 2006

  45. [45]

    H. S. Joet al.(CLAS Collaboration), Cross sections for the exclusive photon electroproduction on the proton and generalized parton distributions, Phys. Rev. Lett. 115, 212003 (2015)

    work page 2015

  46. [46]

    Defurneet al., E00-110 experiment at Jefferson Lab Hall A: Deeply virtual Compton scattering off the pro- ton at 6 GeV, Phys

    M. Defurneet al., E00-110 experiment at Jefferson Lab Hall A: Deeply virtual Compton scattering off the pro- ton at 6 GeV, Phys. Rev. C92, 055202 (2015)

    work page 2015

  47. [47]

    Defurneet al., A glimpse of gluons through deeply virtual Compton scattering on the proton, Nat

    M. Defurneet al., A glimpse of gluons through deeply virtual Compton scattering on the proton, Nat. Com- mun.8, 1408 (2017)

    work page 2017

  48. [48]

    Georgeset al., Deeply virtual Compton scattering cross section at high Bjorkenx B, Phys

    F. Georgeset al., Deeply virtual Compton scattering cross section at high Bjorkenx B, Phys. Rev. Lett.128, 252002 (2022)

    work page 2022

  49. [49]

    Benali, C

    M. Benali, C. Desnault, M. Mazouz, Z. Ahmed, H. Al- bataineh,et al., Deeply virtual Compton scattering off 22 the neutron., Nature Phys.16, 191 (2020)

    work page 2020

  50. [50]

    Akhunzyanov and et al

    R. Akhunzyanov and et al. (COMPASS Collaboration), Phys. Lett. B793, 188 (2019)

    work page 2019

  51. [51]

    G. D. Alexeevet al., Measurement of the hard exclu- siveπ 0 muoproduction cross section at compass, Physics Letters B870, 139832 (2025)

    work page 2025

  52. [52]

    Gautheron and et al

    F. Gautheron and et al. (COMPASS Collaboration), COMPASS-II Proposal, CERN/SPSC-2010-014, SPSC- P340 (2010)

    work page 2010

  53. [53]

    Biselli and et al

    A. Biselli and et al. (CLAS12 Collaboration), E12-06- 119 Experiment Proposal: Deeply Virtual Compton Scattering with CLAS at 11 GeV (2006)

    work page 2006

  54. [54]

    Christiaens, M

    G. Christiaens, M. Defurne,et al.(CLAS Collabora- tion), First CLAS12 Measurement of Deeply Virtual Compton Scattering Beam-Spin Asymmetries in the Ex- tended Valence Region, Phys. Rev. Lett.130, 211902 (2023)

    work page 2023

  55. [55]

    Accardi, J

    A. Accardi, J. Albacete, M. Anselmino,et al., Electron- Ion Collider: The next QCD frontier, Eur. Phys. J. A 52, 268 (2016)

    work page 2016

  56. [56]

    R. A. Khaleket al., Requirements and detector con- cepts for the Electron-Ion Collider: EIC Yellow Report, (2021)

    work page 2021

  57. [57]

    Anderle, V

    D. Anderle, V. Bertone, X. Cao,et al., Electron-ion collider in china, Front. Phys.16, 64701 (2021)

    work page 2021

  58. [58]

    J. L. A. Fernandezet al., A large hadron electron col- lider at CERN report on the physics and design concepts for machine and detector, J. Phys. G39, 075001 (2012)

    work page 2012

  59. [59]

    Deeply virtual Compton scattering at small x_B and the access to the GPD H

    K. Kumeriˇ cki and D. Mueller, Deeply virtual Compton scattering at smallx B and the access to the GPD H, Nucl. Phys. B841, 1 (2010), arXiv:0904.0458 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2010

  60. [60]

    Kumeriˇ cki and D

    K. Kumeriˇ cki and D. M¨ uller, Description and interpre- tation of DVCS measurements, EPJ Web Conf.112, 01012 (2016)

    work page 2016

  61. [61]

    ˇCui´ c, K

    M. ˇCui´ c, K. c. v. Kumeriˇ cki, and A. Sch¨ afer, Separation of quark flavors using deeply virtual compton scattering data, Phys. Rev. Lett.125, 232005 (2020)

    work page 2020

  62. [62]

    S. V. Goloskokov and P. Kroll, The Role of the quark and gluon GPDs in hard vector-meson electroproduc- tion, Eur. Phys. J. C53, 367 (2008), arXiv:0708.3569 [hep-ph]

    work page arXiv 2008

  63. [63]

    Deeply virtual electroproduction of photons and mesons on the nucleon

    M. Vanderhaeghen, P. A. M. Guichon, and M. Guidal, Deeply virtual electroproduction of photons and mesons on the nucleon, Nucl. Phys. A663, 324 (2000), arXiv:hep-ph/9910426

    work page internal anchor Pith review Pith/arXiv arXiv 2000

  64. [64]

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

    K. Kumericki, D. Mueller, and K. Passek-Kumericki, Towards a fitting procedure for deeply virtual Compton scattering at next-to-leading order and beyond, Nucl. Phys. B794, 244 (2008), arXiv:hep-ph/0703179

    work page internal anchor Pith review Pith/arXiv arXiv 2008

  65. [65]

    A fitter code for Deep Virtual Compton Scattering and Generalized Parton Distributions

    M. Guidal, A fitter code for deep virtual compton scat- tering and generalized parton distributions, The Euro- pean Physical Journal A37, 319 (2008), 0807.2355

    work page internal anchor Pith review Pith/arXiv arXiv 2008

  66. [66]

    Guidal and H

    M. Guidal and H. Moutarde, Generalized parton distributions from deeply virtual compton scattering at hermes, The European Physical Journal A42, 10.1140/epja/i2009-10840-4 (2009)

    work page doi:10.1140/epja/i2009-10840-4 2009

  67. [67]

    Guidal, Generalized parton distributions from deep virtual compton scattering at clas, Physics Letters B 689, 156 (2010)

    M. Guidal, Generalized parton distributions from deep virtual compton scattering at clas, Physics Letters B 689, 156 (2010)

    work page 2010

  68. [68]

    Constraints on the $\tilde{H}$ Generalized Parton Distribution from Deep Virtual Compton Scattering Measured at HERMES

    M. Guidal, Constraints on the ˜HGeneralized Parton Distribution from Deep Virtual Compton Scattering Measured at HERMES, Phys. Lett. B693, 17 (2010), arXiv:1005.4922 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2010

  69. [69]

    Generalized Parton Distributions and Deeply Virtual Compton Scattering

    M. Bo¨ er and M. Guidal, Generalized Parton Distribu- tions and Deeply Virtual Compton Scattering, J. Phys. G42, 034023 (2015), arXiv:1412.4651 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2015

  70. [70]

    HERMES impact for the access of Compton form factors

    K. Kumeriˇ cki, D. M¨ uller, and M. Murray, HERMES impact for the access of Compton form factors, Phys. Part. Nucl.45, 723 (2014), arXiv:1301.1230 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2014

  71. [71]

    Extraction of the Compton Form Factor H from DVCS measurements at Jefferson Lab

    H. Moutarde, Extraction of the Compton Form Factor H from DVCS measurements at Jefferson Lab, Phys. Rev. D79, 094021 (2009), arXiv:0904.1648 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2009

  72. [72]

    Neural network generated parametrizations of deeply virtual Compton form factors

    K. Kumericki, D. Mueller, and A. Schafer, Neural network generated parametrizations of deeply virtual Compton form factors, JHEP07, 073, arXiv:1106.2808 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv

  73. [73]

    Unbiased determination of DVCS Compton Form Factors

    H. Moutarde, P. Sznajder, and J. Wagner, Unbiased determination of DVCS Compton Form Factors, Eur. Phys. J. C79, 614 (2019), arXiv:1905.02089 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  74. [74]

    Grigsby, B

    J. Grigsby, B. Kriesten, J. Hoskins, S. Liuti, P. Alonzi, and M. Burkardt, Deep learning analysis of deeply virtual exclusive photoproduction, Phys. Rev. D104, 016001 (2021)

    work page 2021

  75. [75]

    B. D. Ripley,Stochastic Simulation(Wiley, New York, NY, USA, 1987)

    work page 1987

  76. [76]

    GPD phenomenology and DVCS fitting - Entering the high-precision era

    K. Kumericki, S. Liuti, and H. Moutarde, GPD phe- nomenology and DVCS fitting: Entering the high- precision era, Eur. Phys. J. A52, 157 (2016), arXiv:1602.02763 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  77. [77]

    Ji, Parton physics on a euclidean lattice, Phys

    X. Ji, Parton physics on a euclidean lattice, Phys. Rev. Lett.110, 262002 (2013)

    work page 2013

  78. [78]

    Ji, Parton physics from large-momentum effective field theory, Sci

    X. Ji, Parton physics from large-momentum effective field theory, Sci. China. Phys, Mech. & Astron.57, 1407 (2014)

    work page 2014

  79. [79]

    Ji, Y.-S

    X. Ji, Y.-S. Liu, Y. Liu, J.-H. Zhang, and Y. Zhao, Large-momentum effective theory, Rev. Mod. Phys.93, 035005 (2021), arXiv:2004.03543 [hep-ph]

    work page arXiv 2021

  80. [80]

    Y. Guo, F. P. Aslan, X. Ji, and M. G. Santiago, First global extraction of generalized parton distributions from experiment and lattice data with next-to-leading- order accuracy, Physical Review Letters135, 261903 (2025), published 30 December 2025

    work page 2025

Showing first 80 references.