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Study of deeply virtual Compton scattering at the future Electron-Ion Collider

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arxiv 2503.05908 v1 pith:TALL3R2C submitted 2025-03-07 hep-ph hep-exnucl-exnucl-th

Elke-Caroline Aschenauer , Varvara Batozskaya , Salvatore Fazio , Alexander Jentsch , Jihee Kim , Krešimir Kumerički , Herve Moutarde , Kornelija Passek-K.
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Daria Sokhan Hubert Spiesberger Pawe{l} Sznajder Kemal Tezgin
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classification hep-phhep-exnucl-exnucl-th
keywords futurecomptondvcsimpactmeasurementscolliderdeeplydistributions
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

This study presents the impact of future measurements of deeply virtual Compton scattering (DVCS) with the ePIC detector at the electron-ion collider (EIC), currently under construction at Brookhaven National Laboratory. The considered process is sensitive to generalized parton distributions (GPDs), the understanding of which is a cornerstone of the EIC physics programme. Our study marks a milestone in the preparation of DVCS measurements at EIC and provides a reference point for future analyses. In addition to presenting distributions of basic kinematic variables obtained with the latest ePIC design and simulation software, we examine the impact of future measurements on the understanding of nucleon tomography and DVCS Compton form factors, which are directly linked to GPDs. We also assess the impact of radiative corrections and background contribution arising from exclusive $\pi^0$ production.

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Cited by 2 Pith papers

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

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

    hep-ph 2025-12 conditional novelty 5.0 of 10

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

  2. Toward an event-level analysis of hadron structure using differential programming

    hep-ph 2025-07 conditional novelty 4.0 of 10

    LOITS is a differentiable sampling method, demonstrated in a GAN closure test, that maps sampled events back to the parameters of a target density for event-level inference.

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