Big Bang Nucleosynthesis constraints on the cosmological evolution in a Universe with a Weylian Boundary

Teodora M. Matei, Cristian Croitoru, Tiberiu Harko

公開日: 2025/9/1

Abstract

We investigate the effects that arise from the inclusion of Weylian boundary terms in the Einstein gravitational field equations in the Big Bang Nucleosynthesis (BBN) framework. With the help of the generalized Friedmann equations for a Universe with a Weylian boundary, obtained for a Friedmann-Lemaitre-Robertson-Walker FLRW metric, three distinct cosmological models can be constructed. The cosmological evolution is determined by a dissipative scalar field, and by the Weyl vector coming from the boundary. Several cosmological scenarios are obtained via the appropriate splitting of the generalized energy conservation equation. In the present work we obtain relevant constraints on these models by using the BBN data. In particular, the effects on the BBN that arise in the post warm-inflationary era will be examined by theoretically evaluating the measured abundances of relic nuclei (Hydrogen, Deuterium, Helium-3, Helium-4, and Lithium-7). We consider firstly the primordial mass fraction estimates, and their deviations due to changes in the freezing temperature, which impose an upper limit on the effective energy density obtained from the modified Friedmann equations. The deviation from the standard energy density of the radiative plasma is therefore constrained by the abundances of the Helium-4 nuclei. Secondly, an upper limit will be considered in a numerical analysis performed through the usage of the \texttt{PRyMordial} software package, with the help of which we calculate the primordial abundances of the light elements by evaluating the thermonuclear rates within the considered modified gravity framework. Finally, an MCMC analysis will validate the cosmological model with Weylian boundary contributions, imposing relevant constraints on the initial conditions of the cosmos. The methodology is implemented in the python code \texttt{genesys}, which is available on GitHub.

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