Current constraints on the minimally extended varying speed of light model through the cosmic distance duality relation (2505.15768v1)
Abstract: One of the most crucial tests of the standard cosmological model consists on testing possible variations on fundamental physical constants. In frameworks such as the minimally extended varying speed of light model (meVSL), the relationship between the luminosity distance ($D_{\text{L}}$) and the angular diameter distance ($D_{\text{A}}$), namely the cosmic distance duality relation (CDDR), is expected to deviate from $\eta(z) \equiv D_{\text{L}}(z)/D_{\text{A}}(z) = (1+z){2}$, making it a powerful probe of a potential variation of such a fundamental constant. Hence, we test the viability of the meVSL model through the CDDR by comparing $D_{\text{A}}$ measurements, provided by the transverse (2D) and anisotropic (3D) baryon acoustic oscillations (BAO) observations from different surveys, like SDSS, DES and DESI, in combination with $D_{\text{L}}$ measurements from Pantheon+ type Ia Supernova (SNe) compilation. The Gaussian Process reconstruction is employed on the SN data to match $D_{\text{A}}$ with $D_{\text{L}}$ at the same redshift. We find no deviation of the standard CDDR relation within 1-2$\sigma$ confidence level when considering SNe with 2D and 3D BAO samples combined together, as well as when considering SNe with 3D BAO only. However, when SNe and 2D BAO only are considered, the standard CDDR is only recovered at $\sim 4\sigma$ confidence level. However, such a result might be due to some recently discussed tensions between SN and BAO datasets, especially at low redshifts, in addition to possible inconsistencies between the BAO datasets individually. Therefore, our results show no significant evidence in favour of the meVSL model, once these potential systematics are taken into account.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.