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Likelihood and Correlation Analysis of Compton Form Factors for Deeply Virtual Exclusive Scattering on the Nucleon

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arxiv 2410.23469 v2 pith:IS4BWP5N submitted 2024-10-30 hep-ph

Likelihood and Correlation Analysis of Compton Form Factors for Deeply Virtual Exclusive Scattering on the Nucleon

classification hep-ph
keywords comptondeeplylikelihoodunpolarizedvirtualanalysisfactorsform
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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A likelihood analysis of the observables in deeply virtual exclusive photoproduction off a proton target, $ep \rightarrow e' p' \gamma'$, is presented. Two processes contribute to the reaction: deeply virtual Compton scattering, where the photon is produced at the proton vertex, and the Bether-Heitler process, where the photon is radiated from the electron. We consider the unpolarized process for which the largest amount of data with all the kinematic dependences are available from corresponding datasets with unpolarized beams and unpolarized targets from Jefferson Lab. We provide and use a method which derives a joint likelihood of the Compton form factors, which parametrize the deeply virtual Compton scattering amplitude in QCD, for each observed combination of the kinematic variables defining the reaction. The unpolarized twist-two cross section likelihood fully constrains only three of the Compton form factors (CFFs). The impact of the twist-three corrections to the analysis is also explored. The derived likelihoods are explored using Markov chain Monte Carlo (MCMC) methods. Using our proposed method we derive CFF error bars and covariances. Additionally, we explore methods which may reduce the magnitude of error bars/contours in the future.

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Forward citations

Cited by 5 Pith papers

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

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    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.

  3. Markov chain Monte Carlo (MCMC) based Likelihood Extraction of Chiral-Odd Compton Form Factors from Deeply Virtual Exclusive Experiments

    hep-ph 2026-05 conditional novelty 5.0

    Joint MCMC likelihood of JLab cross sections and asymmetries constrains twist-two chiral-odd CFFs and helicity amplitudes for a fixed DVMP kinematic bin.

  4. Assessing the impact of the electron ion collider in China on Deeply Virtual Compton Scattering

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    Projected EicC DVCS asymmetry data would substantially reduce uncertainties on all leading-order Compton form factors, most strongly in the sea-quark region.

  5. Markov chain Monte Carlo (MCMC) based Likelihood Extraction of Chiral-Odd Compton Form Factors from Deeply Virtual Exclusive Experiments

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    MCMC-based joint likelihood extraction constrains chiral-odd CFFs from DVMP cross-sections and asymmetries.