Eddington-inspired Born-Infeld gravity: Constraints from the generalized parton distributions (GPDs)
The MMGPDs Collaboration, Muhammad Goharipour, Anoushiravan Moradi, K. Azizi

TL;DR
This paper uses the internal pressure distribution of the proton, derived from generalized parton distributions, to set constraints on the EiBI gravity parameter, highlighting the importance of precise measurements for testing alternative gravity theories.
Contribution
It introduces a novel method to constrain EiBI gravity using proton pressure profiles from GPDs, providing updated bounds on the theory's parameter $ppa$.
Findings
Constraints on ppa range from 0.10 to 0.3 m^5 kg^{-1} s^{-2}
Proton pressure model choice significantly affects the bounds
Using moments of pressure distribution yields competitive constraints
Abstract
The Eddington-inspired Born-Infeld (EiBI) theory of gravity modifies general relativity in high-density regimes. It offers an alternative framework that avoids cosmological singularities and remodels gravitational dynamics within compact objects. An important feature of EiBI gravity is its additional parameter, , which governs deviations from standard gravitational behavior. In this study, we investigate constraints on using the internal pressure distribution of the proton, derived from gravitational form factor (GFF) obtained through a QCD analysis of generalized parton distributions (GPDs). By comparing pressure profiles extracted from skewness-dependent GPDs with previous determinations based on deeply virtual Compton scattering (DVCS) data, we establish updated bounds on . Our results show that the choice of proton pressure model significantly…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsParticle physics theoretical and experimental studies · Cosmology and Gravitation Theories · Computational Physics and Python Applications
