Deeply virtual Compton scattering off the neutron

Benali, M.; Desnault, C.; Mazouz, M.; Ahmed, Z.; Allada, K.; Aniol, K.; Bellini, Vincenzo; Boeglin, W.; Bertin, P.Y.; Brossard, Michel; Camsonne, A.; Canan, M.; Chandavar, S.; Chen, C.; Chen, J.-P.; Defurne, M.; Jager, C. W. de; Лео, Р. Де; Deur, A.; Fassi, L. El; Ent, R.; Flay, D.; Friend, M.; Fuchey, E.; Frullani, S.; Garibaldi, F.; Gaskell, D.; Giusa, A.; Glamazdin, O.; Golge, S.; Gómez, J.; Hansen, J. O.; Higinbotham, D.; Holmstrom, T.; Horn, T.; Huang, J.; Huang, Min; Huber, G. M.; Hyde, C.; Iqbal, S.; Itard, F.; Kang, H.; Kelleher, A.; Keppel, C.; Koirala, S.; Korover, I.; LeRose, J. J.; Lindgren, R.; Long, E.; Magne, M.; Mammei, J.; Margaziotis, D. J.; Markowitz, P.; Jiménez-Argüello, A. Martí; Meddi, F.; Meekins, D.; Michaels, R.; Mihovilovič, M.; Muangma, N.; Camacho, C. Muñoz; Nadel-Turoński, P.; Nuruzzaman, N.; Paremuzyan, R.; Pomatsalyuk, R.; Puckett, A. J. R.; Punjabi, V.; Qiang, Y.; Rakhman, A.; Rashad, M. N. H.; Riordan, S.; Roche, J.; Russo, G.; Sabatié, F.; Saenboonruang, Kiadtisak; Saha, A.; Sawatzky, B.; Selvy, L.; Shahinyan, A.; Širca, S.; Solvignon, P.; Sperduto, M. L.; Subedi, R.; Sulkosky, V.; Sutera, C.; Tobias, W. A.; Urciuoli, G. M.; Wang, D.; Wojtsekhowski, B.; Yao, Huijun; Ye, Z.; Zana, L.; Zhan, X.; Zhang, J.; Zhao, B.; Zhao, Zhiwen; Zheng, X.; Zhu, P.

doi:10.1038/s41567-019-0774-3

Cited by 24 publications

(12 citation statements)

References 59 publications

(31 reference statements)

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“…This resulted in a clear separation of up and down quark contributions to the leading CFF H , as shown on right panels of Figure 5. Similar separation was attempted by the authors of [18], using local fitting in each kinematic bin, but extracted flavored CFFs were not so cleanly separated in this work. This illustrates both the usefulness of the neural network approach and power of dispersion relations.…”

Section: Separation Of Quark Flavorsmentioning

confidence: 91%

“…Then, we added the data from the recent measurement of DVCS off neutron [18], and attempted to fit the same neural network model, but now with separate parametrization of up and down quark parts, using the isospin symmetry to relate neutron GPDs to proton ones. This resulted in a clear separation of up and down quark contributions to the leading CFF H , as shown on right panels of Figure 5.…”

Section: Separation Of Quark Flavorsmentioning

confidence: 99%

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Revealing proton structure with neural networks

Kumerički¹

2021

Proceedings of 40th International Conference on High Energy Physics — PoS(ICHEP2020)

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Section: Separation Of Quark Flavorsmentioning

confidence: 91%

Section: Separation Of Quark Flavorsmentioning

confidence: 99%

Revealing proton structure with neural networks

Kumerički¹

2021

Proceedings of 40th International Conference on High Energy Physics — PoS(ICHEP2020)

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“…The extraction of single CFFs from experimental data essentially falls into two groups: global [23,24] and local [25,26,27] fits. On the one hand, the former involve the choice of a GPD parameterization and offer a coherent treatment of the measured and unexplored phase-spaces.…”

Section: Compton Form Factorsmentioning

confidence: 99%

Double deeply virtual Compton scattering with positron beams at SoLID

Zhao,

Camsonne,

Marchand

et al. 2021

Preprint

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Double Deeply Virtual Compton Scattering (DDVCS) is the only experimental channel for the determination of the dependence of the Generalized Parton Distributions (GPDs) on both the average and the transferred momentum independently. The physics observables of the electron induced di-muon production reaction #» e ± p → e ± pµ + µ − off unpolarized hydrogen are discussed. Their measurement with the high luminosity and large acceptance SoLID spectrometer at the Thomas Jefferson National Accelerator Facility, using polarized and unpolarized positron and electron beams at 11 GeV is investigated. This experimental configuration is shown to provide unprecedented access to the GPDs with the determination of the real and imaginary parts of the Compton Form Factor H in an unexplored phase space, and to enable an exploratory investigation of higher twist effects.

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“…As first demonstrated in [20] and described theoretically in [29], the measurement of the DVCS cross section at two or more values of the ep center-of-mass energy √ s provides statistically significant separation of the real and imaginary parts of the BH-DVCS interference term as well as the DVCS 2 contribution in the cross sections for polarized electrons. A new analysis [30] of all previous JLab DVCS data followed a similar procedure, and obtained flavor-separated Compton Form Factors, after inclusion of our recent neutron DVCS data [31]. In the present analysis, realistic error bands on the chiral-even CFFs are obtained by explicit inclusion of higher-order terms (e.g.…”

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confidence: 93%

Deeply virtual Compton scattering cross section at high Bjorken $x_B$

Georges,

Rashad,

Stefanko

et al. 2022

Preprint

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We report high-precision measurements of the Deeply Virtual Compton Scattering (DVCS) cross section at high values of the Bjorken variable xB. DVCS is sensitive to the Generalized Parton Distributions of the nucleon, which provide a three-dimensional description of its internal constituents.Using the exact analytic expression of the DVCS cross section for all possible polarization states of the initial and final electron and nucleon, and final state photon, we present the first experimental extraction of all four helicity-conserving Compton Form Factors (CFFs) of the nucleon as a function of xB, while systematically including helicity flip amplitudes. In particular, the high accuracy of the present data demonstrates sensitivity to some very poorly known CFFs.

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Deeply virtual Compton scattering off the neutron

Cited by 24 publications

References 59 publications

Revealing proton structure with neural networks

Revealing proton structure with neural networks

Double deeply virtual Compton scattering with positron beams at SoLID

Deeply virtual Compton scattering cross section at high Bjorken $x_B$

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