“School of Particles And Accelerator”
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| Paper IPM / Particles And Accelerator / 18030 |
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In this paper, we introduce a novel Ansatz for generalized parton distributions (GPDs), named GSAMA24. This Ansatz aims to provide a more accurate and comprehensive description of the internal structure of hadrons by incorporating advanced parametrizations and fitting techniques. We compare the performance of the GSAMA24 Ansatz with three established models: the extended Regge (ER), modified Gaussian (MG), and M-HS22 Ansatz. The GSAMA24 Ansatz is designed to address limitations observed in previous models by offering improved flexibility in the ð¡ dependence and skewness parameter ð. Our analysis involves fitting the GSAMA24 Ansatz to experimental form factor data and evaluating its predictive power against the ER, MG, and M-HS22 models. The comparison is based on key metrics such as the accuracy of form factor predictions, the consistency with known GPD properties, and the computational efficiency of the fitting process. Results indicate that the GSAMA24 Ansatz provides a superior fit to the experimental data, particularly in the high momentum transfer region, where it outperforms the ER and MG models. Additionally, the GSAMA24 Ansatz demonstrates better agreement with the theoretical expectations of GPD behavior compared to the M-HS22 model. These findings suggest that the GSAMA24 Ansatz is a promising tool for future studies of hadronic structure and could significantly enhance our understanding of the spatial and momentum distributions of quarks and gluons within hadrons.
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