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.
Study of deeply virtual Compton scattering at the future Electron-Ion Collider
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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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Toward an event-level analysis of hadron structure using differential programming
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.